DO-MLGAI: Mastering Machine Learning with Generative AI

Generative AI (GenAI) is revolutionizing how machine learning models are built, optimized, and deployed. From predictive modeling to hyperparameter tuning, AI-powered tools like ChatGPT, Gemini, DeepSeek, Claude, and Copilot are enhancing the efficiency and accuracy of ML workflows. This intensive two-day hands-on course will teach participants how to leverage GenAI to build, optimize, and fine-tune machine learning models using Python. Participants will explore how GenAI can assist in data preprocessing, feature engineering, model selection, and hyperparameter tuning. Through practical exercises, they will learn to automate repetitive tasks, improve model accuracy, and enhance workflow efficiency using AI-driven coding techniques.

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.