DO-MLGAI: Mastering Machine Learning with Generative AI

By the end of this course, participants will:

• Understand how Generative AI can accelerate ML model development
• Learn AI-assisted data preprocessing, feature engineering, and model selection
• Explore prompt engineering for generating Python scripts and ML code
• Master hyperparameter tuning techniques using AI-driven optimization
• Build and optimize predictive models with real-world datasets
• Automate ML workflows using GenAI-powered tools

Upon completing this course, participants will be able to:

• Utilize GenAI tools to generate, refine, and optimize machine learning models
• Automate data preprocessing, feature selection, and model evaluation
• Implement AI-assisted hyperparameter tuning for performance optimization
• Leverage AI-generated Python code for ML model development and debugging
• Apply best practices in using AI for predictive analytics and model deployment

• Data scientists and machine learning engineers
• AI and data analysts looking to optimize ML workflows
• Python developers interested in AI-powered model development
• IT professionals and researchers working on predictive analytics
• AI enthusiasts keen on automating machine learning processes

Day 1: Building Predictive Models with AI Assistance

Module 1: Introduction to Generative AI in ML

• Overview of Generative AI and its impact on machine learning
• Key AI tools: ChatGPT, Gemini, DeepSeek, Claude, Copilot – How they assist ML
• Understanding AI-assisted automation in data science

Module 2: Data Preprocessing & Feature Engineering with AI

• AI-powered data cleaning and transformation using Pandas and NumPy
• Automating feature selection and extraction with GenAI
• Hands-on: Using AI tools to preprocess real-world datasets

Module 3: Building Machine Learning Models with Python

• AI-assisted model selection: Choosing the right algorithm
• Generating Python code for ML models using GenAI
• Hands-on: Building and evaluating predictive models with Scikit-learn

Module 4: Fine-Tuning and Debugging ML Models

• Using AI to optimize model architecture and training strategies
• Automating debugging and error handling in Python scripts
• Hands-on: AI-powered performance tuning of ML models

 

Day 2: Hyperparameter Tuning & AI-Driven Optimization

Module 5: Hyperparameter Tuning with AI

• Introduction to hyperparameter tuning and optimization
• Using AI to generate hyperparameter tuning scripts
• Hands-on: Optimizing model performance with Grid Search CV and Randomized Search CV

Module 6: Advanced AI-Assisted Optimization Techniques

• Bayesian optimization, genetic algorithms, and AI-powered tuning
• Exploring DeepSeek and Claude for model improvement
• Hands-on: Applying AI-driven techniques for ML model tuning

Module 7: Automating ML Workflows with AI

• Automating end-to-end ML pipelines using AI-generated scripts
• Integrating AI-driven tools into Jupyter Notebooks
• Hands-on: Deploying an AI-assisted ML pipeline

Module 8: Real-World Applications and Future Trends

• Case studies: How companies use AI to accelerate ML model development
• Ethical considerations and best practices for AI in ML
• Hands-on: Final project – Building and optimizing a predictive model with AI assistance.