Building Intelligent Applications with AI and ML Training Course

Computer Vision

Data Analysis and Visualization

Deep Learning and Neural Networks

Deployment and Scaling

Ethics and Future of AI

Introduction to AI and ML

Lab Project

Machine Learning Models

Natural Language Processing (NLP)

Summary and Next Steps

• AI application deployment strategies
• Scaling AI applications
• Monitoring and maintaining AI systems

• Developing a small-scale intelligent application
• Working with real-world datasets
• Collaborating on a group project to solve an industry-relevant problem

• Ethical considerations in AI
• AI policy and regulation
• Future trends in AI and ML

• Exploratory data analysis
• Data visualization techniques
• Statistical foundations for ML

• Fundamentals of neural networks
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)

• Image processing fundamentals
• Object detection and image classification
• Advanced topics in computer vision

• Overview of AI and ML concepts
• Data collection and preprocessing
• Introduction to Python for AI

• Supervised learning algorithms
• Unsupervised learning algorithms
• Model evaluation and selection

• Text processing and feature extraction
• Sentiment analysis and text classification
• Language models and chatbots