Masterclass in AI and Machine Learning #254002

Upon completion of this course, participants will be able to:

1. Understand advanced AI/ML concepts, including deep learning, reinforcement learning, and generative models.

2. Build, train, and deploy ML models using frameworks like TensorFlow, PyTorch, and Scikit-learn.

3. Apply NLP and computer vision techniques to solve real-world problems.

4. Optimize models for performance, scalability, and interpretability.

5. Implement MLOps practices for continuous integration and deployment (CI/CD) of ML systems.

6. Navigate ethical challenges and ensure responsible AI adoption.

This masterclass is ideal for:

• Data Scientists and ML Engineers

• Software Developers transitioning to AI/ML roles

• AI Researchers and Academic Professionals

• Tech Leads and Product Managers in AI-driven projects

• IT Professionals seeking advanced AI/ML skills

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI and Machine Learning

• Module 1: Introduction to AI/ML: Trends, Opportunities, and Challenges

• Module 2: Supervised vs. Unsupervised Learning: Regression, Classification, Clustering

• Module 3: Data Preprocessing and Feature Engineering

• Hands-On Lab: Building Your First ML Model with Scikit-learn

• Case Study: How Netflix Uses ML for Recommendations

Day 2: Deep Learning and Neural Networks

• Module 4: Neural Networks Fundamentals: Perceptrons, Activation Functions, Backpropagation

• Module 5: Deep Learning Frameworks: TensorFlow and PyTorch

• Module 6: Convolutional Neural Networks (CNNs) for Image Processing

• Hands-On Lab: Image Classification with CNNs

• Workshop: Hyperparameter Tuning and Model Optimization

Day 3: Natural Language Processing (NLP) and Generative AI

• Module 7: NLP Techniques: Tokenization, Embeddings, Transformer Models

• Module 8: Large Language Models (LLMs): GPT, BERT, and Prompt Engineering

• Module 9: Generative AI: GANs, VAEs, and Creative Applications

• Hands-On Lab: Building a Chatbot with Transformer Models

• Case Study: OpenAI’s GPT-4: Capabilities and Limitations

Day 4: MLOps and Model Deployment

• Module 10: MLOps Fundamentals: CI/CD for Machine Learning

• Module 11: Model Deployment: Cloud vs. Edge, Docker, Kubernetes

• Module 12: Monitoring and Maintaining ML Systems

• Hands-On Lab: Deploying a Model on AWS SageMaker/Azure ML

• Group Exercise: Designing an End-to-End MLOps Pipeline

Day 5: Ethics, Capstone Project, and Future Trends

• Module 13: Ethical AI: Bias, Fairness, Transparency, and Regulation

• Module 14: AI in Emerging Fields: Healthcare, Finance, Autonomous Systems

• Module 15: Future of AI: Quantum ML, AGI, and Career Pathways

• Capstone Project: End-to-End AI Solution Development

• Course Wrap-Up: Certifications, Q&A, and Next Steps