Artificial Intelligence Training Courses

Inteligencia Artificial Training

AI, Synthetic Intelligence training

Client Testimonials

Introduction to Drools 6

I liked the logic exercises (writing rules conditions) on the 2nd day.

Jan Janke- CERN

Introduction to Drools 6

I liked the logic exercises (writing rules conditions) on the 2nd day.

Jan Janke- CERN

Introduction to Drools 6

I liked the logic exercises (writing rules conditions) on the 2nd day.

Jan Janke- CERN

Introduction to Drools 6

I liked the logic exercises (writing rules conditions) on the 2nd day.

Jan Janke- CERN

Hadoop for Developers

The trainer clearly understood the subject matter very well. He managed to articulate the subject areas well and demonstrated using practicals how to apply that knowledge.

Matthew Tindall - Knowledgepool

Hadoop for Developers

The trainer clearly understood the subject matter very well. He managed to articulate the subject areas well and demonstrated using practicals how to apply that knowledge.

Matthew Tindall - Knowledgepool

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

WildFly Server Administration

Trainer was excellent.

100% hands on. Very effective way of learning.

Steve Kirkland-Walton - Purple Secure Systems

Introduction to Drools 6

The course was thorough and was better than wandering through the many books and articles found on the web. I liked the hands on approach and feeling of being able to learn by doing and learning from my mistakes. This something we will use for our software development and testing.

Thank you!!!

Martin Arrambide - Sandia National Laboratories

Introduction to Drools 6

The course was thorough and was better than wandering through the many books and articles found on the web. I liked the hands on approach and feeling of being able to learn by doing and learning from my mistakes. This something we will use for our software development and testing.

Thank you!!!

Martin Arrambide - Sandia National Laboratories

Introduction to Drools 6

The course was thorough and was better than wandering through the many books and articles found on the web. I liked the hands on approach and feeling of being able to learn by doing and learning from my mistakes. This something we will use for our software development and testing.

Thank you!!!

Martin Arrambide - Sandia National Laboratories

Introduction to Drools 6

The course was thorough and was better than wandering through the many books and articles found on the web. I liked the hands on approach and feeling of being able to learn by doing and learning from my mistakes. This something we will use for our software development and testing.

Thank you!!!

Martin Arrambide - Sandia National Laboratories

Subcategories

Artificial Intelligence Course Outlines

ID Name Duration Overview
2625 Model MapReduce and Apache Hadoop 14 hours The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers. Data Mining and Business Intelligence Introduction Area of application Capabilities Basics of data exploration Big data What does Big data stand for? Big data and Data mining MapReduce Model basics Example application Stats Cluster model Hadoop What is Hadoop Installation Configuration Cluster settings Architecture and configuration of Hadoop Distributed File System Console tools DistCp tool MapReduce and Hadoop Streaming Administration and configuration of Hadoop On Demand Alternatives
287810 Modelling Decision and Rules with OMG DMN 14 hours This course teaches how to design and execute decisions in rules with OMB DMN (Decision Model and Notation) standard.Introduction to DMN Short history Basic concepts Decision requirements Decision log Scope and uses of DMN (human and automated decision making) Decision Requirements DRG DRD Decision Table Simple Expression Language (S-FEEL) FEEL Overview of Execution Tools available on the market Simple scenarios and workshop for executing the decision tables
2624 Apache Solr - Full-Text Search Server 14 hours The course is intended for IT specialist that want to implement a solution that allows for elastic and efficient searching of big data sources. Introduction Apache Lucene What is Solr Installation Schema and textanalysis Schema modeling schema.xml Configuration Text analysis Working with index Importing data from other resources Indexing documents Querying Solr API Searching Basics of querying Sorting and Filtering Using scoring Functions Request handling Formatting Solr response Faceting Advanced topics Configuring and deploying Solr Integrating Solr with other libraries/technologies Search components Solr and scaling issues
2460 Introduction to the use of neural networks 7 hours The training is aimed at people who want to learn the basics of neural networks and their applications. The Basics Whether computers can think of? Imperative and declarative approach to solving problems Purpose Bedan on artificial intelligence The definition of artificial intelligence. Turing test. Other determinants The development of the concept of intelligent systems Most important achievements and directions of development Neural Networks The Basics Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples XOR problem Lotto? Equities OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed Problems for today Combinatorial explosion and gaming issues Turing test again Over-confidence in the capabilities of computers
85063 Training Neural Network in R 14 hours This course is an introduction to applying neural networks in real world problems using R-project software. Introduction to Neural Networks What are Neural Networks What is current status in applying neural networks Neural Networks vs regression models Supervised and Unsupervised learning Overview of packages available nnet, neuralnet and others differences between packages and itls limitations Visualizing neural networks Applying Neural Networks Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed
287807 Machine Learning Fundamentals with R 14 hours The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
1245 Business Rule Management (BRMS) with Drools 7 hours This course is aimed at enterprise architects, business and system analysts and managers who want to apply business rules to their solution. With Drools you can write your business rules using almost natural language, therefore reducing the gap between business and IT. Short Introduction to Rule Engines Artificial Intelligence Expert Systems What is a Rule Engine? Why use a Rule Engine? Advantages of a Rule Engine When should you use a Rule Engine? Scripting or Process Engines When you should NOT use a Rule Engine Strong and Loose Coupling What are rules? Creating and Implementing Rules Fact Model KIE Spreadsheet Eclipse Domain Specific Language (DSL) Replacing rules with DSL Testing DSL rules jBPM Integration with Drools Fusion What is Complex Event Processing? Short overview on Fusion Rules Testing Testing with KIE Testing with JUnit Integrating Rules with Applications Invoking rules from Java Code
85064 Big Data Business Intelligence for Telecom and Communication Service Providers 35 hours Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following: Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective How Big Data analytic differs from legacy data analytic In-house justification of Big Data -Telco perspective Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies Network failure and service failure analytics from Network meta-data and IPDR Financial analysis-fraud, wastage and ROI estimation from sales and operational data Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization Target Audience Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office. Business Analysts in Telco CFO office managers/analysts Operational managers QA managers Breakdown of topics on daily basis: (Each session is 2 hours) Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Telco. Case Studies from T-Mobile, Verizon etc. Big Data adaptation rate in North American Telco & and how they are aligning their future business model and operation around Big Data BI Broad Scale Application Area Network and Service management Customer Churn Management Data Integration & Dashboard visualization Fraud management Business Rule generation Customer profiling Localized Ad pushing Day-1: Session-2 : Introduction of Big Data-1 Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume. Data Warehouses – static schema, slowly evolving dataset MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc. Hadoop Based Solutions – no conditions on structure of dataset. Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS Batch- suited for analytical/non-interactive Volume : CEP streaming data Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc) Less production ready – Storm/S4 NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database Day-1 : Session -3 : Introduction to Big Data-2 NoSQL solutions KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB) KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB KV Store (Hierarchical) - GT.m, Cache KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua Tuple Store - Gigaspaces, Coord, Apache River Object Database - ZopeDB, DB40, Shoal Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI Varieties of Data: Introduction to Data Cleaning issue in Big Data RDBMS – static structure/schema, doesn’t promote agile, exploratory environment. NoSQL – semi structured, enough structure to store data without exact schema before storing data Data cleaning issues Day-1 : Session-4 : Big Data Introduction-3 : Hadoop When to select Hadoop? STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration) SEMI STRUCTURED data – tough to do with traditional solutions (DW/DB) Warehousing data = HUGE effort and static even after implementation For variety & volume of data, crunched on commodity hardware – HADOOP Commodity H/W needed to create a Hadoop Cluster Introduction to Map Reduce /HDFS MapReduce – distribute computing over multiple servers HDFS – make data available locally for the computing process (with redundancy) Data – can be unstructured/schema-less (unlike RDBMS) Developer responsibility to make sense of data Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS Day-2: Session-1.1: Spark : In Memory distributed database What is “In memory” processing? Spark SQL Spark SDK Spark API RDD Spark Lib Hanna How to migrate an existing Hadoop system to Spark Day-2 Session -1.2: Storm -Real time processing in Big Data Streams Sprouts Bolts Topologies Day-2: Session-2: Big Data Management System Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari In Cloud : Whirr Evolving Big Data platform tools for tracking ETL layer application issues Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI : Introduction to Machine learning Learning classification techniques Bayesian Prediction-preparing training file Markov random field Supervised and unsupervised learning Feature extraction Support Vector Machine Neural Network Reinforcement learning Big Data large variable problem -Random forest (RF) Representation learning Deep learning Big Data Automation problem – Multi-model ensemble RF Automation through Soft10-M LDA and topic modeling Agile learning Agent based learning- Example from Telco operation Distributed learning –Example from Telco operation Introduction to Open source Tools for predictive analytics : R, Rapidminer, Mahut More scalable Analytic-Apache Hama, Spark and CMU Graph lab Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Telecom Insight analytic Visualization analytic Structured predictive analytic Unstructured predictive analytic Customer profiling Recommendation Engine Pattern detection Rule/Scenario discovery –failure, fraud, optimization Root cause discovery Sentiment analysis CRM analytic Network analytic Text Analytics Technology assisted review Fraud analytic Real Time Analytic Day-3 : Sesion-1 : Network Operation analytic- root cause analysis of network failures, service interruption from meta data, IPDR and CRM: CPU Usage Memory Usage QoS Queue Usage Device Temperature Interface Error IoS versions Routing Events Latency variations Syslog analytics Packet Loss Load simulation Topology inference Performance Threshold Device Traps IPDR ( IP detailed record) collection and processing Use of IPDR data for Subscriber Bandwidth consumption, Network interface utilization, modem status and diagnostic HFC information Day-3: Session-2: Tools for Network service failure analysis: Network Summary Dashboard: monitor overall network deployments and track your organization's key performance indicators Peak Period Analysis Dashboard: understand the application and subscriber trends driving peak utilization, with location-specific granularity Routing Efficiency Dashboard: control network costs and build business cases for capital projects with a complete understanding of interconnect and transit relationships Real-Time Entertainment Dashboard: access metrics that matter, including video views, duration, and video quality of experience (QoE) IPv6 Transition Dashboard: investigate the ongoing adoption of IPv6 on your network and gain insight into the applications and devices driving trends Case-Study-1: The Alcatel-Lucent Big Network Analytics (BNA) Data Miner Multi-dimensional mobile intelligence (m.IQ6) Day-3 : Session 3: Big Data BI for Marketing/Sales –Understanding sales/marketing from Sales data: ( All of them will be shown with a live predictive analytic demo ) To identify highest velocity clients To identify clients for a given products To identify right set of products for a client ( Recommendation Engine) Market segmentation technique Cross-Sale and upsale technique Client segmentation technique Sales revenue forecasting technique Day-3: Session 4: BI needed for Telco CFO office: Overview of Business Analytics works needed in a CFO office Risk analysis on new investment Revenue, profit forecasting New client acquisition forecasting Loss forecasting Fraud analytic on finances ( details next session ) Day-4 : Session-1: Fraud prevention BI from Big Data in Telco-Fraud analytic: Bandwidth leakage / Bandwidth fraud Vendor fraud/over charging for projects Customer refund/claims frauds Travel reimbursement frauds Day-4 : Session-2: From Churning Prediction to Churn Prevention: 3 Types of Churn : Active/Deliberate , Rotational/Incidental, Passive Involuntary 3 classification of churned customers: Total, Hidden, Partial Understanding CRM variables for churn Customer behavior data collection Customer perception data collection Customer demographics data collection Cleaning CRM Data Unstructured CRM data ( customer call, tickets, emails) and their conversion to structured data for Churn analysis Social Media CRM-new way to extract customer satisfaction index Case Study-1 : T-Mobile USA: Churn Reduction by 50% Day-4 : Session-3: How to use predictive analysis for root cause analysis of customer dis-satisfaction : Case Study -1 : Linking dissatisfaction to issues – Accounting, Engineering failures like service interruption, poor bandwidth service Case Study-2: Big Data QA dashboard to track customer satisfaction index from various parameters such as call escalations, criticality of issues, pending service interruption events etc. Day-4: Session-4: Big Data Dashboard for quick accessibility of diverse data and display : Integration of existing application platform with Big Data Dashboard Big Data management Case Study of Big Data Dashboard: Tableau and Pentaho Use Big Data app to push location based Advertisement Tracking system and management Day-5 : Session-1: How to justify Big Data BI implementation within an organization: Defining ROI for Big Data implementation Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain Case studies of revenue gain from customer churn Revenue gain from location based and other targeted Ad An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation. Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System: Understanding practical Big Data Migration Roadmap What are the important information needed before architecting a Big Data implementation What are the different ways of calculating volume, velocity, variety and veracity of data How to estimate data growth Case studies in 2 Telco Day-5: Session 3 & 4: Review of Big Data Vendors and review of their products. Q/A session: AccentureAlcatel-Lucent Amazon –A9 APTEAN (Formerly CDC Software) Cisco Systems Cloudera Dell EMC GoodData Corporation Guavus Hitachi Data Systems Hortonworks Huawei HP IBM Informatica Intel Jaspersoft Microsoft MongoDB (Formerly 10Gen) MU Sigma Netapp Opera Solutions Oracle Pentaho Platfora Qliktech Quantum Rackspace Revolution Analytics Salesforce SAP SAS Institute Sisense Software AG/Terracotta Soft10 Automation Splunk Sqrrl Supermicro Tableau Software Teradata Think Big Analytics Tidemark Systems VMware (Part of EMC)
288032 Introduction to Deep Learning 21 hours Backprop, modular models Logsum module RBF Net MAP/MLE loss Parameter Space Transforms Convolutional Module Gradient-Based Learning  Energy for inference, Objective for learning PCA; NLL:  Latent Variable Models Probabilistic LVM Loss Function Handwriting recognition
1264 Artificial Intelligence Overview 7 hours This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development. Artificial Intelligence History Intelligent Agents Problem Solving Solving Problems by Searching Beyond Classical Search Adversarial Search Constraint Satisfaction Problems Knowledge and Reasoning Logical Agents First-Order Logic Inference in First-Order Logic Classical Planning Planning and Acting in the Real World Knowledge Representation Uncertain Knowledge and Reasoning Quantifying Uncertainty Probabilistic Reasoning Probabilistic Reasoning over Time Making Simple Decisions Making Complex Decisions Learning Learning from Examples Knowledge in Learning Learning Probabilistic Models Reinforcement Learning Communicating, Perceiving, and Acting; Natural Language Processing Natural Language for Communication Perception Robotics Conclusions Philosophical Foundations AI: The Present and Future
85066 IoT (Internet of Things) for Entrepreneurs, Managers and Investors 21 hours Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined. In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet. However the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT. Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app. This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business. Course objectives Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one? Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc. Security issues and security solutions for IoT Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc Open source /commercial enterprise cloud platform for IoT-Ayla, iO Bridge, Libellium, Axeda, Cisco frog cloud Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc Target Audience Investors and IoT entrepreneurs Managers and Engineers whose company is venturing into IoT space Business Analysts & Investors Pre-requisites Should have basic knowledge of business operation, devices, electronics systems and data systems Must have basic understanding of software and systems Basic understanding of Statistics ( in Excel levels) 1. Day 1, Session 1 — Business Overview of Why IoT is so important Case Studies from Nest, CISCO and top industries IoT adaptation rate in North American & and how they are aligning their future business model and operation around IoT Broad Scale Application Area Smart House and Smart City Industrial Internet Smart Cars Wearables Home Healthcare Business Rule Generation for IoT 3 layered architecture of Big Data — Physical (Sensors), Communication, and Data Intelligence 2. Day 1, Session 2 — Introduction of IoT: All about Sensors – Electronics Basic function and architecture of a sensor — sensor body, sensor mechanism, sensor calibration, sensor maintenance, cost and pricing structure, legacy and modern sensor network — all the basics about the sensors Development of sensor electronics — IoT vs legacy, and open source vs traditional PCB design style Development of sensor communication protocols — history to modern days. Legacy protocols like Modbus, relay, HART to modern day Zigbee, Zwave, X10,Bluetooth, ANT, etc. Business driver for sensor deployment — FDA/EPA regulation, fraud/tempering detection, supervision, quality control and process management Different Kind of Calibration Techniques — manual, automation, infield, primary and secondary calibration — and their implication in IoT Powering options for sensors — battery, solar, Witricity, Mobile and PoE Hands on training with single silicon and other sensors like temperature, pressure, vibration, magnetic field, power factor etc. 3. Day 1, Session 3 — Fundamental of M2M communication — Sensor Network and Wireless protocol What is a sensor network? What is ad-hoc network? Wireless vs. Wireline network WiFi- 802.11 families: N to S — application of standards and common vendors. Zigbee and Zwave — advantage of low power mesh networking. Long distance Zigbee. Introduction to different Zigbee chips. Bluetooth/BLE: Low power vs high power, speed of detection, class of BLE. Introduction of Bluetooth vendors & their review. Creating network with Wireless protocols such as Piconet by BLE Protocol stacks and packet structure for BLE and Zigbee Other long distance RF communication link LOS vs NLOS links Capacity and throughput calculation Application issues in wireless protocols — power consumption, reliability, PER, QoS, LOS Hands on training with sensor network PICO NET- BLE Base network Zigbee network-master/slave communication Data Hubs : MC and single computer ( like Beaglebone ) based datahub 4. Day 1, Session 4 — Review of Electronics Platform, production and cost projection PCB vs FPGA vs ASIC design-how to take decision Prototyping electronics vs Production electronics QA certificate for IoT- CE/CSA/UL/IEC/RoHS/IP65: What are those and when needed? Basic introduction of multi-layer PCB design and its workflow Electronics reliability-basic concept of FIT and early mortality rate Environmental and reliability testing-basic concepts Basic Open source platforms: Arduino, Raspberry Pi, Beaglebone, when needed? RedBack, Diamond Back 5. Day 2, Session 1 — Conceiving a new IoT product- Product requirement document for IoT State of the present art and review of existing technology in the market place Suggestion for new features and technologies based on market analysis and patent issues Detailed technical specs for new products- System, software, hardware, mechanical, installation etc. Packaging and documentation requirements Servicing and customer support requirements High level design (HLD) for understanding of product concept Release plan for phase wise introduction of the new features Skill set for the development team and proposed project plan -cost & duration Target manufacturing price 6. Day 2, Session 2 — Introduction to Mobile app platform for IoT Protocol stack of Mobile app for IoT Mobile to server integration –what are the factors to look out What are the intelligent layer that can be introduced at Mobile app level ? iBeacon in IoS Window Azure Linkafy Mobile platform for IoT Axeda Xively 7. Day 2, Session 3 — Machine learning for intelligent IoT Introduction to Machine learning Learning classification techniques Bayesian Prediction-preparing training file Support Vector Machine Image and video analytic for IoT Fraud and alert analytic through IoT Bio –metric ID integration with IoT Real Time Analytic/Stream Analytic Scalability issues of IoT and machine learning What are the architectural implementation of Machine learning for IoT 8. Day 2, Session 4 — Analytic Engine for IoT Insight analytic Visualization analytic Structured predictive analytic Unstructured predictive analytic Recommendation Engine Pattern detection Rule/Scenario discovery — failure, fraud, optimization Root cause discovery 9. Day 3, Session 1 — Security in IoT implementation Why security is absolutely essential for IoT Mechanism of security breach in IOT layer Privacy enhancing technologies Fundamental of network security Encryption and cryptography implementation for IoT data Security standard for available platform European legislation for security in IoT platform Secure booting Device authentication Firewalling and IPS Updates and patches 10. Day 3, Session 2 — Database implementation for IoT : Cloud based IoT platforms SQL vs NoSQL-Which one is good for your IoT application Open sourced vs. Licensed Database Available M2M cloud platform Axeda Xively Omega NovoTech Ayla Libellium CISCO M2M platform AT &T M2M platform Google M2M platform 11. Day 3, Session 3 — A few common IoT systems Home automation Energy optimization in Home Automotive-OBD IoT-Lock Smart Smoke alarm BAC ( Blood alcohol monitoring ) for drug abusers under probation Pet cam for Pet lovers Wearable IOT Mobile parking ticketing system Indoor location tracking in Retail store Home health care Smart Sports Watch 12. Day 3, Session 4 — Big Data for IoT 4V- Volume, velocity, variety and veracity of Big Data Why Big Data is important in IoT Big Data vs legacy data in IoT Hadoop for IoT-when and why? Storage technique for image, Geospatial and video data Distributed database Parallel computing basics for IoT
2503 Statistics with SPSS Predictive Analytics Software 14 hours Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques. Using the program The dialog boxes input / downloading data the concept of variable and measuring scales preparing a database Generate tables and graphs formatting of the report Command language syntax automated analysis storage and modification procedures create their own analytical procedures Data Analysis descriptive statistics Key terms: eg variable, hypothesis, statistical significance measures of central tendency measures of dispersion measures of central tendency standardization Introduction to research the relationships between variables correlational and experimental methods Summary: This case study and discussion
284980 From Data to Decision with Big Data and Predictive Analytics 21 hours Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview Data Sources Minding Data Recommender systems Target Marketing Datatypes Structured vs unstructured Static vs streamed Attitudinal, behavioural and demographic data Data-driven vs user-driven analytics data validity Volume, velocity and variety of data Models Building models Statistical Models Machine learning Data Classification Clustering kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Neural networks Markov Model Regression Ensemble methods ROI Benefit/Cost ratio Cost of software Cost of development Potential benefits Building Models Data Preparation (MapReduce) Data cleansing Choosing methods Developing model Testing Model Model evaluation Model deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources
85065 Big Data Business Intelligence for Govt. Agencies 40 hours Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies: Mobile devices and applications Cloud services Social business technologies and networking Big Data and analytics IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers. Each session is 2 hours Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Govt. Case Studies from NIH, DoE Big Data adaptation rate in Govt. Agencies & and how they are aligning their future operation around Big Data Predictive Analytics Broad Scale Application Area in DoD, NSA, IRS, USDA etc. Interfacing Big Data with Legacy data Basic understanding of enabling technologies in predictive analytics Data Integration & Dashboard visualization Fraud management Business Rule/ Fraud detection generation Threat detection and profiling Cost benefit analysis for Big Data implementation Day-1: Session-2 : Introduction of Big Data-1 Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume. Data Warehouses – static schema, slowly evolving dataset MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc. Hadoop Based Solutions – no conditions on structure of dataset. Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS Batch- suited for analytical/non-interactive Volume : CEP streaming data Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc) Less production ready – Storm/S4 NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database Day-1 : Session -3 : Introduction to Big Data-2 NoSQL solutions KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB) KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB KV Store (Hierarchical) - GT.m, Cache KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua Tuple Store - Gigaspaces, Coord, Apache River Object Database - ZopeDB, DB40, Shoal Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI Varieties of Data: Introduction to Data Cleaning issue in Big Data RDBMS – static structure/schema, doesn’t promote agile, exploratory environment. NoSQL – semi structured, enough structure to store data without exact schema before storing data Data cleaning issues Day-1 : Session-4 : Big Data Introduction-3 : Hadoop When to select Hadoop? STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration) SEMI STRUCTURED data – tough to do with traditional solutions (DW/DB) Warehousing data = HUGE effort and static even after implementation For variety & volume of data, crunched on commodity hardware – HADOOP Commodity H/W needed to create a Hadoop Cluster Introduction to Map Reduce /HDFS MapReduce – distribute computing over multiple servers HDFS – make data available locally for the computing process (with redundancy) Data – can be unstructured/schema-less (unlike RDBMS) Developer responsibility to make sense of data Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS Day-2: Session-1: Big Data Ecosystem-Building Big Data ETL: universe of Big Data Tools-which one to use and when? Hadoop vs. Other NoSQL solutions For interactive, random access to data Hbase (column oriented database) on top of Hadoop Random access to data but restrictions imposed (max 1 PB) Not good for ad-hoc analytics, good for logging, counting, time-series Sqoop - Import from databases to Hive or HDFS (JDBC/ODBC access) Flume – Stream data (e.g. log data) into HDFS Day-2: Session-2: Big Data Management System Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari In Cloud : Whirr Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI : Introduction to Machine learning Learning classification techniques Bayesian Prediction-preparing training file Support Vector Machine KNN p-Tree Algebra & vertical mining Neural Network Big Data large variable problem -Random forest (RF) Big Data Automation problem – Multi-model ensemble RF Automation through Soft10-M Text analytic tool-Treeminer Agile learning Agent based learning Distributed learning Introduction to Open source Tools for predictive analytics : R, Rapidminer, Mahut Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Govt. Insight analytic Visualization analytic Structured predictive analytic Unstructured predictive analytic Threat/fraudstar/vendor profiling Recommendation Engine Pattern detection Rule/Scenario discovery –failure, fraud, optimization Root cause discovery Sentiment analysis CRM analytic Network analytic Text Analytics Technology assisted review Fraud analytic Real Time Analytic Day-3 : Sesion-1 : Real Time and Scalable Analytic Over Hadoop Why common analytic algorithms fail in Hadoop/HDFS Apache Hama- for Bulk Synchronous distributed computing Apache SPARK- for cluster computing for real time analytic CMU Graphics Lab2- Graph based asynchronous approach to distributed computing KNN p-Algebra based approach from Treeminer for reduced hardware cost of operation Day-3: Session-2: Tools for eDiscovery and Forensics eDiscovery over Big Data vs. Legacy data – a comparison of cost and performance Predictive coding and technology assisted review (TAR) Live demo of a Tar product ( vMiner) to understand how TAR works for faster discovery Faster indexing through HDFS –velocity of data NLP or Natural Language processing –various techniques and open source products eDiscovery in foreign languages-technology for foreign language processing Day-3 : Session 3: Big Data BI for Cyber Security –Understanding whole 360 degree views of speedy data collection to threat identification Understanding basics of security analytics-attack surface, security misconfiguration, host defenses Network infrastructure/ Large datapipe / Response ETL for real time analytic Prescriptive vs predictive – Fixed rule based vs auto-discovery of threat rules from Meta data Day-3: Session 4: Big Data in USDA : Application in Agriculture Introduction to IoT ( Internet of Things) for agriculture-sensor based Big Data and control Introduction to Satellite imaging and its application in agriculture Integrating sensor and image data for fertility of soil, cultivation recommendation and forecasting Agriculture insurance and Big Data Crop Loss forecasting Day-4 : Session-1: Fraud prevention BI from Big Data in Govt-Fraud analytic: Basic classification of Fraud analytics- rule based vs predictive analytics Supervised vs unsupervised Machine learning for Fraud pattern detection Vendor fraud/over charging for projects Medicare and Medicaid fraud- fraud detection techniques for claim processing Travel reimbursement frauds IRS refund frauds Case studies and live demo will be given wherever data is available. Day-4 : Session-2: Social Media Analytic- Intelligence gathering and analysis Big Data ETL API for extracting social media data Text, image, meta data and video Sentiment analysis from social media feed Contextual and non-contextual filtering of social media feed Social Media Dashboard to integrate diverse social media Automated profiling of social media profile Live demo of each analytic will be given through Treeminer Tool. Day-4 : Session-3: Big Data Analytic in image processing and video feeds Image Storage techniques in Big Data- Storage solution for data exceeding petabytes LTFS and LTO GPFS-LTFS ( Layered storage solution for Big image data) Fundamental of image analytics Object recognition Image segmentation Motion tracking 3-D image reconstruction Day-4: Session-4: Big Data applications in NIH: Emerging areas of Bio-informatics Meta-genomics and Big Data mining issues Big Data Predictive analytic for Pharmacogenomics, Metabolomics and Proteomics Big Data in downstream Genomics process Application of Big data predictive analytics in Public health Big Data Dashboard for quick accessibility of diverse data and display : Integration of existing application platform with Big Data Dashboard Big Data management Case Study of Big Data Dashboard: Tableau and Pentaho Use Big Data app to push location based services in Govt. Tracking system and management Day-5 : Session-1: How to justify Big Data BI implementation within an organization: Defining ROI for Big Data implementation Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain Case studies of revenue gain from saving the licensed database cost Revenue gain from location based services Saving from fraud prevention An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation. Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System: Understanding practical Big Data Migration Roadmap What are the important information needed before architecting a Big Data implementation What are the different ways of calculating volume, velocity, variety and veracity of data How to estimate data growth Case studies Day-5: Session 4: Review of Big Data Vendors and review of their products. Q/A session: Accenture APTEAN (Formerly CDC Software) Cisco Systems Cloudera Dell EMC GoodData Corporation Guavus Hitachi Data Systems Hortonworks HP IBM Informatica Intel Jaspersoft Microsoft MongoDB (Formerly 10Gen) MU Sigma Netapp Opera Solutions Oracle Pentaho Platfora Qliktech Quantum Rackspace Revolution Analytics Salesforce SAP SAS Institute Sisense Software AG/Terracotta Soft10 Automation Splunk Sqrrl Supermicro Tableau Software Teradata Think Big Analytics Tidemark Systems Treeminer VMware (Part of EMC)
284990 Applied Machine Learning 14 hours This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Bayesian Graphical Models Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) for regression and classification Boosting Ensemble models Neural networks Hidden Markov Models (HMM) Space State Models Clustering
287766 Programming with Big Data in R 21 hours Introduction to Programming Big Data with R (bpdR) Setting up your environment to use pbdR Scope and tools available in pbdR Packages commonly used with Big Data alongside pbdR Message Passing Interface (MPI) Using pbdR MPI 5 Parallel processing Point-to-point communication Send Matrices Summing Matrices Collective communication Summing Matrices with Reduce Scatter / Gather Other MPI communications Distributed Matrices Creating a distributed diagonal matrix SVD of a distributed matrix Building a distributed matrix in parallel   Statistics Applications Monte Carlo Integration Reading Datasets Reading on all processes Broadcasting from one process Reading partitioned data Distributed Regression Distributed Bootstrap 
287822 Predictive Models with PMML 7 hours The course is created to scientific, developers, analysts or any other people who want to standardize or exchange their models with Predictive Model Markup Language (PMML) file format.Predictive Models Intro to predictive models Predictive models supported by PMML PMML Elements Header Data Dictionary Data Transformations Model Mining Schema Targets Output API Overview of API providers for PMML Executing your model in a cloud
284994 Managing Business Rules with PHP Business Rules 14 hours This course explain how to write declarative rules using PHP Business Rules (http://sourceforge.net/projects/phprules/). It shows how to write, organize and integrate rules with existing code. Most of the course is based on exercises preceded with short introduction and examples. Short Introduction to Rule Engines Artificial Intelligence  Expert Systems What is a Rule Engine? Why use a Rule Engine? Advantages of a Rule Engine When should you use a Rule Engine? Scripting or Process Engines When you should NOT use a Rule Engine Strong and Loose Coupling What are rules? Creating and Implementing Rules Fact Model Rule independence Priority, flags and processes Executing rules Integrating rules with existing applications and Rule Maintenance Rule integration PHP Unit tests and automated testing DDD and TDD with Business rules  
287768 Introduction to Recommendation Systems 7 hours Audience Marketing department employees, IT strategists and other people involved in decisions related to the design and implementation of recommender systems. Format Short theoretical background follow by analysing working examples and short, simple exercises. Challenges related to data collection Information overload Data types (video, text, structured data, etc...) Potential of the data now and in the near future Basics of Data Mining Recommendation and searching Searching and Filtering Sorting Determining weights of the search results Using Synonyms Full-text search Long Tail Chris Anderson idea Drawbacks of Long Tail Determining Similarities Products Users Documents and web sites Content-Based Recommendation i measurement of similarities Cosine distance The Euclidean distance vectors TFIDF and frequency of terms Collaborative filtering Community rating Graphs Applications of graphs  Determining similarity of graphs Similarity between users Neural Networks Basic concepts of Neural Networks Training Data and Validation Data Neural Network examples in recommender systems How to encourage users to share their data Making systems more comfortable Navigation Functionality and UX Case Studies Popularity of recommender systems and their problems Examples
287823 Data Mining with R 14 hours Sources of methods Artificial intelligence Machine learning Statistics Sources of data Pre processing of data Data Import/Export Data Exploration and Visualization Dimensionality Reduction Dealing with missing values R Packages Data mining main tasks Automatic or semi-automatic analysis of large quantities of data Extracting previously unknown interesting patterns groups of data records (cluster analysis) unusual records (anomaly detection) dependencies (association rule mining) Data mining Anomaly detection (Outlier/change/deviation detection) Association rule learning (Dependency modeling) Clustering Classification Regression Summarization Frequent Pattern Mining Text Mining Decision Trees Regression Neural Networks Sequence Mining Frequent Pattern Mining Data dredging, data fishing, data snooping
287841 Apache Mahout for Developers 14 hours Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases. Implementing Recommendation Systems with Mahout Introduction to recommender systems Representing recommender data Making recommendation Optimizing recommendation Clustering Basics of clustering Data representation Clustering algorithms Clustering quality improvements Optimizing clustering implementation Application of clustering in real world Classification Basics of classification Classifier training Classifier quality improvements
287782 Apache Spark 14 hours Why Spark? Problems with Traditional Large-Scale Systems Introducing Spark Spark Basics What is Apache Spark? Using the Spark Shell Resilient Distributed Datasets (RDDs) Functional Programming with Spark Working with RDDs RDD Operations Key-Value Pair RDDs MapReduce and Pair RDD Operations The Hadoop Distributed File System Why HDFS? HDFS Architecture Using HDFS Running Spark on a Cluster Overview A Spark Standalone Cluster The Spark Standalone Web UI Parallel Programming with Spark RDD Partitions and HDFS Data Locality Working With Partitions Executing Parallel Operations Caching and Persistence RDD Lineage Caching Overview Distributed Persistence Writing Spark Applications Spark Applications vs. Spark Shell Creating the SparkContext Configuring Spark Properties Building and Running a Spark Application Logging Spark, Hadoop, and the Enterprise Data Center Overview Spark and the Hadoop Ecosystem Spark and MapReduce Spark Streaming Spark Streaming Overview Example: Streaming Word Count Other Streaming Operations Sliding Window Operations Developing Spark Streaming Applications Common Spark Algorithms Iterative Algorithms Graph Analysis Machine Learning Improving Spark Performance Shared Variables: Broadcast Variables Shared Variables: Accumulators Common Performance Issues
2431 Introduction to Nools 7 hours Flows Defining A Flow Sessions Facts Assert Retract Modify Retrieving Facts Firing Disposing Removing A Flow Removing All Flows Checking If A Flow Exists Agenda Group Focus Auto Focus Conflict Resolution Defining Rules Structure Salience Scope Constraints Not Or From Exists Actions Async Actions Globals Import Browser Support
287846 OptaPlanner in Practice 21 hours Planner introduction What is OptaPlanner? What is a planning problem? Use Cases and examples Bin Packaging Problem Example Problem statement Problem size Domain model diagram Main method Solver configuration Domain model implementation Score configuration Travelling Salesman Problem (TSP) Problem statement Problem size Domain model Main method Chaining Solver configuration Domain model implementation Score configuration Planner configuration Overview Solver configuration Model your planning problem Use the Solver Score calculation Score terminology Choose a Score definition Calculate the Score Score calculation performance tricks Reusing the Score calculation outside the Solver Optimization algorithms Search space size in the real world Does Planner find the optimal solution? Architecture overview Optimization algorithms overview Which optimization algorithms should I use? SolverPhase Scope overview Termination SolverEventListener Custom SolverPhase Move and neighborhood selection Move and neighborhood introduction Generic Move Selectors Combining multiple MoveSelectors EntitySelector ValueSelector General Selector features Custom moves Construction heuristics First Fit Best Fit Advanced Greedy Fit Cheapest insertion Regret insertion Local search Local Search concepts Hill Climbing (Simple Local Search) Tabu Search Simulated Annealing Late Acceptance Step counting hill climbing Late Simulated Annealing (experimental) Using a custom Termination, MoveSelector, EntitySelector, ValueSelector or Acceptor Evolutionary algorithms Evolutionary Strategies Genetic Algorithms Hyperheuristics Exact methods Brute Force Depth-first Search Benchmarking and tweaking Finding the best Solver configuration Doing a benchmark Benchmark report Summary statistics Statistics per dataset (graph and CSV) Advanced benchmarking Repeated planning Introduction to repeated planning Backup planning Continuous planning (windowed planning) Real-time planning (event based planning) Drools Short introduction to Drools Writing Score Function in Drools Integration Overview Persistent storage SOA and ESB Other environment
287792 Hadoop Administration on MapR 28 hours Audience: IT professionals who aspire to get involved in the 'Big Data' world or require knowledge of open source NoSQL solutions. This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand. Big Data Overview: What is Big Data Why Big Data is gaining popularity Big Data Case Studies Big Data Characteristics Solutions to work on Big Data. Hadoop & Its components: What is Hadoop and what are its components. Hadoop Architecture and its characteristics of Data it can handle /Process. Brief on Hadoop History, companies using it and why they have started using it. Hadoop Frame work & its components- explained in detail. What is HDFS and Reads -Writes to Hadoop Distributed File System. How to Setup Hadoop Cluster in different modes- Stand- alone/Pseudo/Multi Node cluster. (This includes setting up a Hadoop cluster in VM BOX/VMware, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster). What is Map Reduce frame work and how it works. Running Map Reduce jobs on Hadoop cluster. Understanding Replication , Mirroring and Rack awareness in context of Hadoop clusters. Hadoop Cluster Planning:   How to plan your hadoop cluster.   Understanding hardware-software to plan your hadoop cluster.   Understanding workloads and planning cluster to avoid failures and perform optimum. What is MapR and why MapR :  Overview of MapR and its architecture. Understanding & working of MapR Control System, MapR Volumes , snapshots & Mirrors. Planning a cluster in context of MapR. Comparison of MapR with other distributions and Apache Hadoop. MapR installation and cluster deployment. Cluster Setup & Administration: Managing services, nodes ,snapshots, mirror volumes and remote clusters. Understanding and managing Nodes. Understanding of Hadoop components, Installing Hadoop components alongside MapR Services. Accessing Data on cluster including via NFS Managing services & nodes. Managing data by using volumes,  managing users and groups, managing & assigning roles to nodes, commissioning decommissioning of nodes, cluster administration and performance monitoring, configuring/ analyzing and monitoring metrics to monitor performance, configuring and administering MapR security. Understanding and working with M7- Native storage for MapR tables. Cluster configuration and tuning for optimum performance. Cluster upgrade and integration with other setups: Upgrading software version of MapR and types of upgrade. Configuring Mapr cluster to access HDFS cluster. Setting up MapR cluster on Amazon Elastic Mapreduce. All the above topics include Demonstrations and practice sessions for learners to have hands on experience of the technology.
287862 Designing Inteligent User Interface with HTML5, JavaScript and Rule Engines 21 hours Coding interfaces which allow users to get what they want easily is hard. This course guides you how to create effective UI with newest technologies and libraries. It introduces idea of coding logic in Rule Engines (mostly Nools and PHP Rules) to make it easier to modify and test. After that the courses shows a way of integrating the logic on the front end of the website using JavaScript. Logic coded this way can be reused on the backend. Writing your rules Available rule engines Stating rules in a declarative manner Extending rules Create unit tests for the rules Available test frameworks Running tests automatically Creating GUI for the rules Available frameworks GUI design principles Integrating logic with the GUI Running rules in the browser Ajax Decision tables Create functional tests for the GUI Available frameworks Testing against multiple browsers
287847 Data Mining 21 hours Course can be provided with any tools, including free open-source data mining software and applicationsIntroduction Data mining as the analysis step of the KDD process ("Knowledge Discovery in Databases") Subfield of computer science Discovering patterns in large data sets Sources of methods Artificial intelligence Machine learning Statistics Database systems What is involved? Database and data management aspects Data pre-processing Model and inference considerations Interestingness metrics Complexity considerations Post-processing of discovered structures Visualization Online updating Data mining main tasks Automatic or semi-automatic analysis of large quantities of data Extracting previously unknown interesting patterns groups of data records (cluster analysis) unusual records (anomaly detection) dependencies (association rule mining) Data mining Anomaly detection (Outlier/change/deviation detection) Association rule learning (Dependency modeling) Clustering Classification Regression Summarization Use and applications Able Danger Behavioral analytics Business analytics Cross Industry Standard Process for Data Mining Customer analytics Data mining in agriculture Data mining in meteorology Educational data mining Human genetic clustering Inference attack Java Data Mining Open-source intelligence Path analysis (computing) Police-enforced ANPR in the UK Reactive business intelligence SEMMA Stellar Wind Talx Zapaday Data dredging, data fishing, data snooping
287794 Hadoop Administration 32 hours A basic knowledge of linux/unix would be helpful. Basic knowledge on Java know hows or databases will be helpful. Note**Even if not,students can learn from scratch to professional level. Audience: IT or non IT professionals who are interested in growing their career by gaining knowledge about BIG DATA and framework/solutions such as Hadoop & its components. Format: Course would have theoretical discussions followed by environment setup and tasks to work on to have hands on experience. Every student gets a 24*7 support for 15 days during and after course completion, course material, knowledge of real time case studies. 40% theory 55% hands on experience through instructor led live demons and then assignments. 5% test and mock interviews. Topics Covered: Big Data Overview: - What is Big Data - Why Big Data is gaining popularity - Big Data Case Studies - Big Data Characteristics - Solutions to work on Big Data. Hadoop & Its components: - What is Hadoop and what are its components. - Hadoop Architecture and its characteristics of Data it can handle /Process. - Brief on Hadoop History, companies using it and why they have started using it. Hadoop Frame work & its components- explained in detail. - What is HDFS and Reads -Writes to Hadoop Distributed File System. - How to Setup Hadoop Cluster in different modes- Pseudo/Multi Node cluster. This includes setting up Hadoop cluster in VM BOX/VMware or on individual machines, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster. - What is Map Reduce frame work and how it works. - Running Map Reduce jobs on Hadoop cluster. - Understanding Replication , Mirroring and Rack awareness in context of Hadoop . All the above topics include Demos and practice sessions for learners to have hands on experience on the technology. Hadoop Cluster Planning: - How to plan your hadoop cluster. - Understanding hardware-software to plan your hadoop cluster. - Understanding workloads and planning cluster to avoid failures and perform optimum. Working with Hadoop cluster- Hadoop Administration - Understanding functionalities of JOB TRACKER –resource management and Job scheduling. - Understanding Schedulers- Fair | FIFO | capacity scheduler - Hadoop Administration: Setting paramters to Setup  Trash | Schedulers & pool | Metadata & Data storage at specific locations | replication | Hadoop client | commissioning and decommissioning of data nodes and many more. - Hadoop Administration commands to work on Hadoop clusters: Balancer | Job List, Status, Setting priority | Save namespace | Metasave | DFSadmin commands | FS commands | distcp | fsck |setting space quota | write /read access to HDFS | securing Hadoop cluster and many more. - Backup and recovery - Analyzing problems and resolving them : Some examples from live real time environments : Hadoop daemons not starting up | namespace IDs out of sync | connectivity issues between slave and master nodes | data being under replicated | browsing through respective UIs | job failures | etc. Hadoop cluster with latest features: - Hadoop 1.x and 2.x differences - Hadoop 2.x new features - What is Yarn, Federation and high Availability? - Hadoop daemons and what has changed. Working on Hadoop 2.x cluster: - Upgrading Hadoop old versions ( 0.22.x or 1.X.X) to Hadoop 2.X in different modes - Setting up Hadoop 2.x clusters in different modes and verifying the setup. - Running Map Reduce jobs on Hadoop Yarn. - A revisit on Hadoop configuration files, deprecated parameters, add on’s to existing config files and miscellaneous. - Understanding QJM-Quorum Journal Manager Understanding and working with Hadoop Components: Components: - What is oozie, flume, Hive, Hbase and why are they used. - How to setup Hive/PIG/Hbase on Hadoop clusters. - Setting up and working with HIVE, HBASE, PIG on Hadoop 1.x or Hadoop 2.x - Some real time case studies. - What is cloudera Manager and how is it used. - How to run a mapreduce jobs from Java API. Summarizing and brief revision. Questions and Answers. Depending on client /attendee's requirements , below mentioned topics can be included in the course agenda. Hbase administration in detail. Hive administration in detail. Hadoop administration on AWS. Additional sessions on Pig/Perl/Python scripting, usage of Java APIs with Hadoop cluster.
284991 Introduction to Machine Learning 7 hours This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Ridge regression Clustering
287853 Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours DAY 1 - ARTIFICIAL NEURAL NETWORKS   Introduction and ANN Structure. Biological neurons and artificial neurons. Model of an ANN. Activation functions used in ANNs. Typical classes of network architectures . Mathematical Foundations and Learning mechanisms. Re-visiting vector and matrix algebra. State-space concepts. Concepts of optimization. Error-correction learning. Memory-based learning. Hebbian learning. Competitive learning. Single layer perceptrons. Structure and learning of perceptrons. Pattern classifier - introduction and Bayes' classifiers. Perceptron as a pattern classifier. Perceptron convergence. Limitations of a perceptrons. Feedforward ANN. Structures of Multi-layer feedforward networks. Back propagation algorithm. Back propagation - training and convergence. Functional approximation with back propagation. Practical and design issues of back propagation learning. Radial Basis Function Networks. Pattern separability and interpolation. Regularization Theory. Regularization and RBF networks. RBF network design and training. Approximation properties of RBF. Competitive Learning and Self organizing ANN.  General clustering procedures. Learning Vector Quantization (LVQ). Competitive learning algorithms and architectures. Self organizing feature maps. Properties of feature maps. Fuzzy Neural Networks.  Neuro-fuzzy systems. Background of fuzzy sets and logic. Design of fuzzy stems. Design of fuzzy ANNs. Applications A few examples of Neural Network applications, their advantages and problems will be discussed.    DAY -2 MACHINE LEARNING The PAC Learning Framework Guarantees for finite hypothesis set – consistent case  Guarantees for finite hypothesis set – inconsistent case  Generalities  Deterministic cv.  Stochastic scenarios  Bayes error noise  Estimation and approximation errors  Model selection  Radmeacher Complexity and VC – Dimension  Bias - Variance tradeoff  Regularisation Over-fitting  Validation   Support Vector Machines   Kriging (Gaussian Process regression) PCA and Kernel PCA  Self Organisation Maps (SOM) Kernel induced vector space  Mercer Kernels and Kernel - induced similarity metrics  Reinforcement Learning    DAY 3 - DEEP LEARNING This will be taught in relation to the topics covered on Day 1 and Day 2 Logistic and Softmax Regression Sparse Autoencoders Vectorization, PCA and Whitening Self-Taught Learning Deep Networks Linear Decoders Convolution and Pooling Sparse Coding Independent Component Analysis Canonical Correlation Analysis Demos and Applications
287809 Fundamentals of Cassandra DB 21 hours This course introduces the basics of Cassandra 2.0 including its installation & configuration, internal architecture, tools, Cassandra Query Language, and administration. Audience Administrators and developers seeking to use Cassandra. This course serves as a foundation and prerequisite for other advanced Cassandra courses.   Introduction to Cassandra Big Data Common use cases of Cassandra Cassandra architecture Installation and Configuration Running and Stopping Cassandra instance Cassandra Data Model Cassandra Query Language Configuring the Cassandra nodes and clusters using CCM cqlsh shell commands nodetool Using cassandra-stress to populate and test the Cassandra nodes Coordinating the Cassandra requests Replication Consistency Tuning Cassandra Nodes Communication Writing and Reading data to/from the storage engine Data directories Anti-entropy operations Cassandra Compaction Choosing and Implementing compaction strategies Best practices in hardware planning Troubleshooting resources
287808 Machine Learning Fundamentals with Python 14 hours The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Course and outline author: Mihaly Barasz Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
2432 Introduction to Drools 6 21 hours This 3 days course is aimed to introduce Drools 6 to developers as well as business analysts. Short introduction to rule engines Short history or Expert Systems and Rules Engine What is Artificial Intelligence? Forward vs Backward chaining Declarative vs procedure/oop Comparison of solutions When to use rule engines? When not to use rule engines? Alternatives to rule engines KIE Authoring Assets Workbench Integration Executing rules directly from KIE Deployment Decision tables Rule Templates Guided rule editor Testing Work Items Versioning and deployment A bit more about repository (git) Developing simple process with rules Writing rules in Eclipse Stateless vs Stateful sessions Selecting proper facts Basic operators and Drools specific operators ) Basic accumulate functions (sum, max, etc...) ​Intermediate calculations Inserting new facts Exercises (lots of them) Ordering rules with BPMN Salience Ruleflow vs BPMN 2.0 Executing ruleset from a process Rules vs gateways Short overview of BPMN 2.0 features (transactions, exception handling) Comprehensive declarative business logic in Drools Domain Specific Languages (DSL) Creating new languages Preparing DSL to be used by manages Basic Natural Language Processing (NLP) with DSL Fusion (CPE), temporal reasoning (for events to happen after, between, etc...) Fusion operators Example in Event Schedules Unit testing Optional Topics OptaPlanner jBPM Drools and integration via web services Drools integration via command line How to change rules/process after deployment without compiling
287818 Hadoop for Developers 14 hours Introduction What is Hadoop? What does it do? How does it do it? The Motivation for Hadoop Problems with Traditional Large-Scale Systems Introducing Hadoop Hadoopable Problems Hadoop: Basic Concepts and HDFS The Hadoop Project and Hadoop Components The Hadoop Distributed File System Introduction to MapReduce MapReduce Overview Example: WordCount Mappers Reducers Hadoop Clusters and the Hadoop Ecosystem Hadoop Cluster Overview Hadoop Jobs and Tasks Other Hadoop Ecosystem Components Writing a MapReduce Program in Java Basic MapReduce API Concepts Writing MapReduce Drivers, Mappers, and Reducers in Java Speeding Up Hadoop Development by Using Eclipse Differences Between the Old and New MapReduce APIs Writing a MapReduce Program Using Streaming Writing Mappers and Reducers with the Streaming API Unit Testing MapReduce Programs Unit Testing The JUnit and MRUnit Testing Frameworks Writing Unit Tests with MRUnit Running Unit Tests Delving Deeper into the Hadoop API Using the ToolRunner Class Setting Up and Tearing Down Mappers and Reducers Decreasing the Amount of Intermediate Data with Combiners Accessing HDFS Programmatically Using The Distributed Cache Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners Practical Development Tips and Techniques Strategies for Debugging MapReduce Code Testing MapReduce Code Locally by Using LocalJobRunner Writing and Viewing Log Files Retrieving Job Information with Counters Reusing Objects Creating Map-Only MapReduce Jobs Partitioners and Reducers How Partitioners and Reducers Work Together Determining the Optimal Number of Reducers for a Job Writing Customer Partitioners Data Input and Output Creating Custom Writable and Writable-Comparable Implementations Saving Binary Data Using SequenceFile and Avro Data Files Issues to Consider When Using File Compression Implementing Custom InputFormats and OutputFormats Common MapReduce Algorithms Sorting and Searching Large Data Sets Indexing Data Computing Term Frequency — Inverse Document Frequency Calculating Word Co-Occurrence Performing Secondary Sort Joining Data Sets in MapReduce Jobs Writing a Map-Side Join Writing a Reduce-Side Join Integrating Hadoop into the Enterprise Workflow Integrating Hadoop into an Existing Enterprise Loading Data from an RDBMS into HDFS by Using Sqoop Managing Real-Time Data Using Flume Accessing HDFS from Legacy Systems with FuseDFS and HttpFS An Introduction to Hive, Imapala, and Pig The Motivation for Hive, Impala, and Pig Hive Overview Impala Overview Pig Overview Choosing Between Hive, Impala, and Pig An Introduction to Oozie Introduction to Oozie Creating Oozie Workflows
288031 Machine Learning for Robotics 21 hours This course introduce machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software. Regression Probabilistic Graphical Models Boosting Kernel Methods Gaussian Processes Evaluation and Model Selection Sampling Methods Clustering CRFs Random Forests IVMs
2618 WildFly Server Administration 14 hours This course is created for Administrators, Developers or anyone who is interested in managing WildFly Application Server (AKA JBoss Application Server). This course usually runs on the newest version of the Application Server, but it can be tailored (as a private course) to older versions starting from version 5.1. Module 1: Installing Core Components Installing the Java environment  Installing JBoss AS Application server features Creating a custom server configuration Module 2: Customizing JBoss AS Services How to monitor JBoss AS services JBoss AS thread pool Configuring logging services Configuring the connection to the database Configuring the transaction service Module 3. Deploying EJB 3 Session Beans Developing Enterprise JavaBeans Configuring the EJB container Module 4: Deploying a Web Application Developing web layout Configuring JBoss Web Server Module 5: Deploying Applications with JBoss Messaging Service The new JBoss Messaging system Developing JMS applications Advanced JBoss Messaging Module 6: Managing JBoss AS Introducing Java Management Extension JBoss AS Administration Console Managing applications Administering resources
287849 Administrator Training for Apache Hadoop 35 hours Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration. 1: HDFS (17%) Describe the function of HDFS Daemons Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing. Identify current features of computing systems that motivate a system like Apache Hadoop. Classify major goals of HDFS Design Given a scenario, identify appropriate use case for HDFS Federation Identify components and daemon of an HDFS HA-Quorum cluster Analyze the role of HDFS security (Kerberos) Determine the best data serialization choice for a given scenario Describe file read and write paths Identify the commands to manipulate files in the Hadoop File System Shell 2: YARN and MapReduce version 2 (MRv2) (17%) Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons Understand basic design strategy for MapReduce v2 (MRv2) Determine how YARN handles resource allocations Identify the workflow of MapReduce job running on YARN Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN. 3: Hadoop Cluster Planning (16%) Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster. Analyze the choices in selecting an OS Understand kernel tuning and disk swapping Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario 4: Hadoop Cluster Installation and Administration (25%) Given a scenario, identify how the cluster will handle disk and machine failures Analyze a logging configuration and logging configuration file format Understand the basics of Hadoop metrics and cluster health monitoring Identify the function and purpose of available tools for cluster monitoring Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig Identify the function and purpose of available tools for managing the Apache Hadoop file system 5: Resource Management (10%) Understand the overall design goals of each of Hadoop schedulers Given a scenario, determine how the FIFO Scheduler allocates cluster resources Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN Given a scenario, determine how the Capacity Scheduler allocates cluster resources 6: Monitoring and Logging (15%) Understand the functions and features of Hadoop’s metric collection abilities Analyze the NameNode and JobTracker Web UIs Understand how to monitor cluster Daemons Identify and monitor CPU usage on master nodes Describe how to monitor swap and memory allocation on all nodes Identify how to view and manage Hadoop’s log files Interpret a log file
2143 Semantic Web Overview 7 hours The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. Semantic Web Overview Introduction Purpose Standards Ontology Projects Resource Description Framework (RDF) Introduction Motivation and Goals RDF Concepts RDF Vocabulary URI and Namespace (Normative) Datatypes (Normative) Abstract Syntax (Normative) Fragment Identifiers
263581 Drools Rules Administration 21 hours This course has been prepared for people who are involved in administering corporate knowledge assets (rules, process) like system administrators, system integrators, application server administrators, etc... We are using the newest stable community version of Drools to run this course, but older versions are also possible if agreed before booking.Drools Administration Short Introduction to Rule Engines Artificial Intelligence Expert Systems What is a Rule Engine? Why use a Rule Engine? Advantages of a Rule Engine When should you use a Rule Engine? Scripting or Process Engines When you should NOT use a Rule Engine Strong and Loose Coupling What are rules? Where things are Managing rules in a jar file Git repository Executing rules from KIE Managing BPMN and workflows files Moving knowledge files (rules, processes, forms, work times...) Rules Testing Where to store test How to execute tests Testing with JUnit Deployment Strategies stand alone application Invoking rules from Java Code integration via files (json, xml, etc...) integration via web services using KIE for integration Administration of rules authoring Packages Artifact Repository Asset Editor Validation Data Model Categories versioning Domain Specific Languages Optimizing hardware and software for rules execution Multithreading and Drools Kie Projects structures Lifecycles Building Deploying Running Installation and Deployment Cheat Sheets Organization Units Users, Rules and Permissions Authentication Repositories Backup and Restore Logging
287850 Hadoop Administration 21 hours The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment Course goal: Getting knowledge regarding Hadoop cluster administration Introduction to Cloud Computing and Big Data solutions Apache Hadoop evolution: HDFS, MapReduce, YARN Installation and configuration of Hadoop in Pseudo-distributed mode Running MapReduce jobs on Hadoop cluster Hadoop cluster planning, installation and configuration Hadoop ecosystem: Pig, Hive, Sqoop, HBase Big Data future: Impala, Cassandra
2617 Natural Language Processing 21 hours This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well. The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc... For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source. Short Introduction to NLP methods word and sentence tokenization text classification sentiment analysis spelling correction information extraction parsing meaning extraction question answering Overview of NLP theory probability statistics machine learning n-gram language modeling naive bayes maxent classifiers sequence models (Hidden Markov Models) probabilistic dependency constituent parsing vector-space models of meaning
287977 MATLAB Fundamental 21 hours This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include: Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Visualizing vector and matrix data Working with data files Working with data types Automating commands with scripts Writing programs with logic and flow control Writing functions Part 1 A Brief Introduction to MATLAB Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you An Example: C vs. MATLAB MATLAB Product Overview MATLAB Application Fields What MATLAB can do for you? The Course Outline Working with the MATLAB User Interface Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes. MATALB Interface Reading data from file Saving and loading variables Plotting data Customizing plots Calculating statistics and best-fit line Exporting graphics for use in other applications Va​riables and Expressions Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables. Entering commands Creating variables Getting help Accessing and modifying values in variables Creating character variables Analysis and Visualization with Vectors Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command. Calculations with vectors Plotting vectors Basic plot options Annotating plots Analysis and Visualization with Matrices Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications. Size and dimensionality Calculations with matrices Statistics with matrix data Plotting multiple columns Reshaping and linear indexing Multidimensional arrays Part 2 Automating Commands with Scripts Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical. A Modelling Example The Command History Creating script files Running scripts Comments and Code Cells Publishing scripts Working with Data Files Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats. Importing data Mixed data types Cell arrays Conversions amongst numerals, strings, and cells Exporting data Multiple Vector Plots Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data. Graphics structure Multiple figures, axes, and plots Plotting equations Using color Customizing plots Logic and Flow Control Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user. Logical operations and variables Logical indexing Programming constructs Flow control Loops Matrix and Image Visualization Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images. Scattered Interpolation using vector and matrix data 3-D matrix visualization 2-D matrix visualization Indexed images and colormaps True color images Part 3 Data Analysis Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command. Dealing with missing data Correlation Smoothing Spectral analysis and FFTs Solving linear systems of equations Writing Functions Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables. Why functions? Creating functions Adding comments Calling subfunctions Workspaces  Subfunctions Path and precedence Data Types Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized. MATLAB data types Integers Structures Converting types File I/O Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files. Opening and closing files Reading and writing text files Reading and writing binary files Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification. Conclusion Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification. Objectives: Summarise what we have learnt A summary of the course Other upcoming courses on MATLAB Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.
2550 Managing Business Logic with Drools 21 hours This course is aimed at enterprise architects, business and system analysts, technical managers and developers who want to apply business rules to their solutions. This course contains a lot of simple hands-on exercises during which the participants will create working rules. Please refer to our other courses if you just need an overview of Drools. This course is usually delivered on the newest stable version of Drools and jBPM, but in case of a bespoke course, can be tailored to a specific version. Short Introduction to Rule Engines Artificial Intelligence  Expert Systems What is a Rule Engine? Why use a Rule Engine? Advantages of a Rule Engine When should you use a Rule Engine? Scripting or Process Engines When you should NOT use a Rule Engine Strong and Loose Coupling What are rules? Creating and Implementing Rules Fact Model KIE Rules visioning and repository Exercises Domain Specific Language (DSL) Replacing rules with DSL Testing DSL rules Exercises jBPM Integration with Drools Short overview of basic BPMN Invoking rules from a processes Grouping rules Exercises Fusion What is Complex Event Processing? Short overview on Fusion Exercises Mvel - the rule language Filtering (fact type, field Operators Compound conditions Operators priority Accumulate Functions (average, min, max, sum, collectList, etc....) Rete - under the hood Compilation algorithm Drools RETE extensions Node Types Understating Rete Tree Rete Optimization Rules Testing Testing with KIE Testing with JUnit OptaPlanner An overview of OptaPlanner Simple examples Integrating Rules with Applications Invoking rules from Java Code
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