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Machine Learning Bootcamp: Python, Projects & Deployment
Bestseller
2,634 students
Machine Learning Bootcamp: Python, Projects & Deployment
Learn Python, Math, Machine Learning, Build Real-World Projects & Deploy ML Apps on AWS
Created bySiddhardhan S
Last updated 1/2026
English
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.
Explore related topics
Course content
18 sections • 128 lectures • 66h 30m total length
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.