Machine Learning Bootcamp: Python, Projects & Deployment

Learn Python, Math, Machine Learning, Build Real-World Projects & Deploy ML Apps on AWS

What you’ll learn
✓ Build machine learning models using Python, covering classification, regression, and unsupervised learning.
✓ Understand the math behind machine learning, including linear algebra, statistics, probability, and calculus with clear intuition.
✓ Perform data collection, EDA, preprocessing, feature engineering, and model evaluation using real-world datasets.
✓ Apply cross-validation, hyperparameter tuning, and model selection to build reliable and optimized ML models.
✓ Convert ML notebooks into production-ready Python scripts and serve models using FastAPI and Streamlit.
✓ Deploy complete, end-to-end machine learning applications on AWS EC2 with real-world workflows.

Requirements
● No prior Machine Learning experience is required. You will learn everything from scratch.
● No advanced math background is needed. All required math concepts are explained with intuition and examples.
● Basic computer skills and willingness to learn and practice are sufficient.
● A laptop or desktop with internet access (Windows, macOS, or Linux).
● No paid software required. All tools used are free and open-source.
● Some sections involve AWS deployment. An AWS account is helpful but optional.

Description
This is a complete, hands-on Machine Learning bootcamp designed to take you from Python basics to building and deploying real-world, production-ready ML applications.

You will learn Machine Learning the right way – starting with Python and essential math foundations, working with real datasets, building models, evaluating them correctly, and finally deploying ML systems on AWS.

Unlike theory-heavy courses, this bootcamp focuses on practical understanding, clean code, real projects, and real deployment workflows used in industry.

What you will gain from this course

• Strong Python programming skills for Machine Learning

• Clear intuition for math behind ML including linear algebra, statistics, calculus, and probability

• Hands-on experience with data collection, EDA, and preprocessing

• Build and evaluate classification, regression, and unsupervised models

• Proper model validation, cross-validation, and optimization techniques

• Multiple real-world Machine Learning projects

• Convert notebooks into clean, production-style Python scripts

• Build ML APIs using FastAPI and UIs using Streamlit

• Deploy complete ML applications on AWS EC2

• Work on production-grade capstone projects you can showcase in your portfolio

Who this course is for

• Beginners starting Machine Learning from scratch

• Students preparing for ML or data science roles

• Professionals transitioning into Machine Learning

• Developers who want to build and deploy real ML applications

No prior Machine Learning, Python or math background is required. Everything is explained step by step with intuition and hands-on examples.

By the end of this bootcamp, you will not just understand Machine Learning —

you will be able to build, deploy, and explain real ML systems with confidence.

Who this course is for
■ Beginners who want to learn Machine Learning from scratch with Python and clear step-by-step guidance.
■ Students and freshers preparing for careers in Machine Learning, Data Science, or AI.
■ Working professionals looking to transition into Machine Learning or upskill with real-world projects.
■ Software developers who want to add Machine Learning and deployment skills to their toolkit.
■ Learners who want to build and deploy real, production-ready ML applications instead of just notebooks.