Mastering Machine Learning with Generative AI
This intensive 2-day hands-on course teaches participants how to leverage Generative AI to build, optimize, and automate machine learning workflows in Python. Learn AI-powered data preprocessing, model building, and hyperparameter tuning using real-world datasets.
Duration:
Fees with SST:
2 Days
RM 2,700.00
Machine learning is evolving — and Generative AI is now a vital part of every data scientist’s toolkit. From predictive modeling to code generation and workflow automation, AI tools like ChatGPT, Gemini, Claude, DeepSeek, and GitHub Copilot are transforming how models are built and deployed.
This 2-day instructor-led course provides an end-to-end learning experience in AI-assisted machine learning development. Participants will learn how to automate data preprocessing, feature engineering, model training, and tuning using Python.
With guided hands-on labs, learners will integrate Generative AI into their existing ML workflows — accelerating productivity, enhancing model performance, and reducing coding complexity.
By the end of the course, participants will have built and optimized predictive models powered by AI-generated Python code, ready for deployment in real-world environments.
Module 1: Introduction to Generative AI in ML
Overview of Generative AI and its role in machine learning; how tools like ChatGPT, Gemini, Claude, and Copilot assist data professionals.
Module 2: Data Preprocessing & Feature Engineering with AI
AI-assisted data cleaning, transformation, and feature extraction using Python and GenAI tools.
Hands-on: Preprocess real-world datasets with AI-powered guidance.
Module 3: Building Machine Learning Models with Python
Generate and refine ML model code automatically; compare models and metrics using AI-assisted workflows.
Hands-on: Build and evaluate predictive models using Scikit-learn.
Module 4: Fine-Tuning and Debugging ML Models
Leverage AI for debugging, optimization, and performance tuning.
Hands-on: Improve ML model performance through AI-driven feedback.
Module 5: Hyperparameter Tuning with AI
Use AI tools to create tuning scripts and optimize model parameters.
Hands-on: Perform tuning with Grid Search CV, Randomized Search CV, and AI-generated optimizations.
Module 6: Advanced AI-Assisted Optimization Techniques
Explore Bayesian optimization, genetic algorithms, and AI-guided performance enhancements.
Hands-on: Apply DeepSeek and Claude for advanced tuning exercises.
Module 7: Automating ML Workflows with AI
Use Generative AI to automate model pipelines and integrate multiple tools.
Hands-on: Build an AI-driven ML pipeline within Jupyter Notebooks.
Module 8: Real-World Applications and Future Trends
Examine case studies, ethics, and emerging technologies in AI-powered ML development.
Hands-on: Final project — Build and optimize a predictive model with AI guidance.
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Frequently Asked Questions (FAQs)
Do I need AI experience?
No. You’ll learn to use Generative AI tools as part of your ML workflow.
What Python libraries are used?
Pandas, NumPy, Scikit-learn, and relevant optimization frameworks.
Is this course project-based?
Yes. Every module includes practical hands-on projects and case studies.
Do we use ChatGPT or Gemini directly?
Yes. Participants will apply both for real-time code generation and optimization.
Does this cover ethical AI?
Yes. Responsible AI and governance best practices are integrated throughout.