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Machine Learning: Practical Foundations

Understand and build machine learning models, from linear regression to neural networks, with real projects.

4.8(5,600 ratings)Rated 4.8 out of 5. Jump to reviews.23,400 students

Created by Rohan Kapoor

  • Last updated Aug 2026
  • English
  • English captions
  • Intermediate level

What you'll learn

  • Choose the right algorithm for a problem
  • Train, tune and evaluate models
  • Avoid overfitting and data leakage
  • Build neural networks with PyTorch
  • Work with images and text
  • Deploy a model as an API

Skills you'll gain

  • Machine Learning
  • scikit-learn
  • PyTorch
  • Model Evaluation

Requirements

  • Python and basic Pandas
  • Comfort with high-school algebra

Who this course is for

  • Data analysts moving into ML
  • Developers curious about AI

Description

A practical, intuition-first introduction to machine learning. You will implement key algorithms, use scikit-learn and PyTorch, and learn how to evaluate models honestly.

Sections
5
Lessons
130
Hours of video
23
Projects
1

Course curriculum

5 sections 130 lessons 23h 20m total length

  • Video: What is machine learning8m
  • Video: Training and testing11m
  • Video: Bias and variance9m
  • Quiz: Foundations quiz5m
  • + 20 more lessons in this section

Projects you'll build

Real, portfolio-ready work. Each project is reviewed against a checklist so you know it meets a professional bar.

  1. Project 01

    House price predictor

    An end-to-end regression model served through a small web API.

Certificate

Earn a certificate employers can verify

  • Issued when you finish every lesson and the final project
  • Unique ID with a public verification page
  • Add it to LinkedIn and your resume in one click
See a verified certificate

Your instructor

Portrait of Rohan Kapoor

Rohan Kapoor

Data Scientist & ML Engineer · 8 years in industry

  • 4.8 instructor rating
  • 29.1K reviews
  • 164K students
  • 4 courses

Rohan builds machine learning systems in production and loves making maths feel practical.

Expect real datasets, clear explanations and models you actually deploy, not just notebooks.

  • Python
  • Data Science
  • Machine Learning
  • SQL
  • Statistics
View full profile

Learner reviews

4.8

Course rating · 5.6K ratings

Rated 4.8 out of 5 from 5600 ratings.
  • 5stars84%
  • 4stars14%
  • 3stars2%
  • 2stars0%
  • 1star0%

  • Ananya Sharma

    5 out of 5 stars,

    The projects made all the difference. I finally understand how the pieces fit together and I have a portfolio I am proud of.

  • Karthik R.

    5 out of 5 stars,

    Clear explanations, no fluff. Every module ends with something practical, which kept me motivated through the whole course.

  • Meera Iyer

    4 out of 5 stars,

    Great structure and very approachable for beginners. A few lessons could be shorter, but the Q&A support was excellent.

  • Vikram S.

    5 out of 5 stars,

    I used the capstone project in my interviews and landed my first developer role. Worth every rupee.

  • Sana Q.

    5 out of 5 stars,

    Loved the pacing and the quizzes. The downloadable resources are genuinely useful at work.

Showing the 5 most recent of 5.6K reviews.

Frequently asked questions

Intermediate level. Before you start: Python and basic Pandas; Comfort with high-school algebra. Everything else is taught step by step inside the course.

Still have a question? Visit the Help Center or contact us.

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