DO-MLGAI: Mastering Machine Learning with Generative AI

Name: DO-MLGAI: Mastering Machine Learning with Generative AI
Brand: AI and Machine Learning

RM2,500.00

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Step into the future of data science with a game-changing 2-day course designed to supercharge your machine learning (ML) skills. "Mastering Machine Learning with Generative AI: Build, Optimize & Automate with Python" combines hands-on learning with cutting-edge AI tools—like ChatGPT, Gemini, Claude, DeepSeek, and Copilot—to teach you how to automate, accelerate, and optimize ML workflows using Python.

Whether you're a data scientist, Python developer, or an AI enthusiast, this course equips you to:

Automate data preprocessing, feature selection, and model debugging
Use AI-generated Python code to build real-world predictive models
Leverage AI for hyperparameter tuning and performance optimization
Deploy AI-assisted ML pipelines that save time and improve accuracy

Why should you or your company enrol?

Because in today’s high-speed tech landscape, manual ML processes are outdated. This training helps individuals and organizations achieve up to 40% faster development cycles and significantly improved model accuracy by harnessing AI to handle repetitive and error-prone coding tasks. With real-world datasets and live coding exercises, you’ll build projects that mirror what top data teams are doing today.

Training Duration: 2 Days

Certificate Of Completion Available
Group Private Class
VILT Class Available
SBL-Khas Claimable

Download Course Outline

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.

DO-MLGAI: Mastering Machine Learning with Generative AI

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