Machine Learning Bootcamp: Build ML models using GenAI

Last updated on November 18, 2025 11:08 am
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Description

What you’ll learn

  • Build a strong foundation in Python, statistics, and machine learning—covering regression, classification, and model evaluation
  • Work with real datasets to clean, preprocess, and visualize data using NumPy, Pandas, and Seaborn for ML readiness
  • Implement core ML algorithms in Python with ChatGPT-assisted coding for faster, cleaner, and more efficient development
  • Master advanced ML techniques like ensemble methods, grid search, and SVMs to create high-performing predictive models
  • Handle missing values, outliers, categorical variables, and feature scaling to improve model quality and accuracy
  • Leverage ChatGPT to explain complex ML concepts, debug Python code, and generate efficient solutions in real-time
  • Compare multiple models side-by-side to select the best fit for predictive accuracy and business requirements

If you’re an aspiring data scientist, analyst, or AI enthusiast looking to break into one of the most in-demand fields of the decade, imagine having a hands-on guide that teaches you not only the theory—but also how to code, implement, and fine-tune models—without getting lost in complexity. What if you could accelerate your learning curve by having an AI partner (ChatGPT) that helps you write cleaner code, debug faster, and understand concepts more intuitively?

In this immersive, practical bootcamp, you’ll gain the technical skills, problem-solving mindset, and project experience needed to work confidently with real-world machine learning applications. Whether you’re building predictive models, classifying data, or tuning advanced algorithms, this course equips you to move from “learning about ML” to “building with ML” in record time.

In this hands-on course, you will:

  • Master the full ML workflow – from data import, exploration, and preprocessing to model building, evaluation, and optimization.

  • Understand the math and logic behind key algorithms like Linear & Logistic Regression, Decision Trees, Random Forests, KNN, SVM, Boosting methods, and more.

  • Learn with ChatGPT-assisted coding – using AI to generate, optimize, and debug Python code for faster, more accurate implementation.

  • Work with Python’s top ML libraries like NumPy, Pandas, Seaborn, Scikit-learn, and XGBoost.

  • Build both regression and classification models and understand when to apply each.

  • Gain experience in advanced topics like model tuning with Grid Search, feature engineering, ensemble methods, and kernel-based SVMs.

Throughout the course, you’ll:

  • Use ChatGPT to write and refine Python code for ML tasks.

  • Explore side-by-side the theory of an algorithm and its real Python implementation.

  • Work with real-world datasets, handling missing values, outliers, and categorical variables.

  • Compare and evaluate models to select the best approach for a given problem.

  • Build a portfolio-ready set of projects that showcase both coding ability and ML understanding.

Machine Learning is more than just knowing algorithms—it’s about applying them effectively to real data. By the end of this bootcamp, you’ll be able to confidently approach ML problems, build and optimize models, and leverage AI tools like ChatGPT to boost your productivity and accuracy.

Whether you’re preparing for a career in data science, adding ML to your skill set as a developer, or simply exploring the potential of AI-powered problem solving, you’ll walk away with the skills, confidence, and workflow to succeed.

Enroll today to build the future—one model at a time—powered by Python, guided by AI, and driven by data.

Who this course is for:

  • Aspiring data scientists and analysts who want a structured, hands-on introduction to machine learning using Python.
  • Students and professionals from non-technical backgrounds eager to break into the ML/AI field with the help of ChatGPT-assisted coding.
  • Software developers, engineers, and IT professionals looking to expand their skill set into machine learning and predictive analytics.
  • Business analysts, managers, and domain experts who want to use data-driven models to solve real-world problems.
  • Anyone curious about how to apply machine learning—from regression to advanced ensemble methods—without needing prior ML experience.

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