Course Outline
Foundations of Machine Learning
- Introduction to Machine Learning concepts and workflows
- Supervised vs. unsupervised learning
- Evaluating machine learning models: metrics and techniques
Bayesian Methods
- Naive Bayes and multinomial models
- Bayesian categorical data analysis
- Bayesian graphical models
Regression Techniques
- Linear regression
- Logistic regression
- Generalized Linear Models (GLM)
- Mixed models and additive models
Dimensionality Reduction
- Principal Component Analysis (PCA)
- Factor Analysis (FA)
- Independent Component Analysis (ICA)
Classification Methods
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM) for regression and classification
- Boosting and ensemble models
Neural Networks
- Introduction to neural networks
- Applications of deep learning in classification and regression
- Training and tuning neural networks
Advanced Algorithms and Models
- Hidden Markov Models (HMM)
- State Space Models
- EM Algorithm
Clustering Techniques
- Introduction to clustering and unsupervised learning
- Popular clustering algorithms: K-Means, Hierarchical Clustering
- Use cases and practical applications of clustering
Summary and Next Steps
Requirements
- Basic understanding of statistics and data analysis
- Programming experience in R, Python, or other relevant programming languages
Audience
- Data scientists
- Statisticians
Testimonials (5)
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
Luke Pontin
Course - Data and Analytics - from the ground up
The flexible and friendly style. Learning exactly what was useful and relevant for me.