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Fundamentals of Machine Learning
Rating: 4.0 out of 5(4 ratings)
417 students

Fundamentals of Machine Learning

Code your own ML models.
Created byShreyan Mitra, Samanyu Goyal, Bruhatt Rao, CodeWithPurpose __
Last updated 4/2026
English

What you'll learn

  • Build a real machine learning model from scratch that predicts house prices using actual code
  • Build a spam email classifier that makes live yes or no decisions on emails it has never seen before
  • Understand the difference between regression and classification and when to use each one
  • Write and run Python code in Kaggle with zero setup or installation required
  • Read and interpret model outputs like weights, accuracy scores, and confusion matrices
  • Understand how the theory from Part 1 shows up line by line in real working code
  • Debug errors in your own code and understand what went wrong and why
  • Walk away with two complete projects you actually built yourself

Course content

3 sections • 5 lectures • 56m total length

  • House Prices ML Model (Video 1)13:48
  • House Prices ML Model (Video 2)12:36

Requirements

  • No prior coding experience needed, everything is explained line by line. Completion of Intro to Machine Learning Part 1 is highly reccomended.

Description

You understood the theory. Now it is time to build.

This is Introduction to Machine Learning Part 2, the hands on continuation of the CodeWithPurpose machine learning series. If Part 1 gave you the foundation, Part 2 is where you turn that knowledge into something real. Actual code. Actual data. Actual models that learn and make predictions on their own.

No prior coding experience needed. No downloads. No complicated setup. Everything runs directly in Kaggle, a free browser based environment, and every single line of code is explained clearly so nothing ever feels like a mystery.

In this course you will build:

  • A House Price Predictor that learns the relationship between a home's features and its sale price

  • A Spam Email Classifier that reads emails and decides in real time whether they are spam or legitimate

Along the way you will learn:

  • The difference between regression and classification, two of the most important problem types in machine learning

  • How to prepare and split data correctly to avoid overfitting

  • How to train, evaluate, and test a model on data it has never seen before

  • How to read model outputs like weights, accuracy scores, and confusion matrices

  • How to debug real errors in your own code just like a professional would

This course is produced by CodeWithPurpose, a global nonprofit on a mission to make world class tech education free and accessible to anyone, anywhere. Over 1,800 students across 110 countries have learned with us, and every course we make will always be completely free.

If you are ready to go from understanding machine learning to actually doing it, this is your next step.

Who this course is for:

  • Beginners who want their very first real coding experience to be meaningful and project based

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