Free Machine Learning Course — scikit-learn, Regression to Neural Nets
Learn machine learning free — supervised vs unsupervised learning, preprocessing, linear & logistic regression, evaluation metrics, overfitting & regularization, decision trees, random forests, K-Means and intro neural networks, with real scikit-learn code.
TL;DR: Learn machine learning free — supervised vs unsupervised learning, preprocessing, linear & logistic regression, evaluation metrics, overfitting & regu…
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Free Machine Learning course: 11 lessons, from the basics through to the advanced topics interviewers actually ask about. Every lesson is free, needs no sign-up, and runs in your browser with an editor and instant AI feedback.
Machine Learning Course Syllabus — 11 Lessons
Work through them in order, or jump to the topic you need. 11 lessons include worked explanations and runnable examples.
Foundations
- What is Machine Learning? — Machine Learning is a branch of AI where computers learn patterns from data without being explicitly programmed.
- The ML Workflow & Train/Test Split — The core ML workflow is: (1) Data collection and labeling, (2) Exploratory analysis to understand your data, (3) Preprocessing (cleaning, scaling, encoding), (4) Feature engineering (creating useful input variables), (5) Train/test…
- Data Preprocessing: Cleaning, Scaling & Encoding — Raw data is messy: missing values, outliers, inconsistent formats, and categorical text.
Supervised Learning
- Linear Regression: Predicting Continuous Values — Linear regression predicts a continuous output (like price or temperature) by fitting a line (or hyperplane in multiple dimensions) through data.
- Logistic Regression & Binary Classification — Logistic regression predicts binary outcomes (0/1, yes/no, pass/fail) using the logistic sigmoid function: p = 1 / (1 + e^(-z)), where z = w*x + b.
- Decision Trees & Random Forests — Decision trees split data recursively on features to create a tree of decisions.
Evaluation & Diagnostics
- Evaluation Metrics: Accuracy, Precision, Recall & F1 — For classification, accuracy (% correct) is intuitive but misleading for imbalanced data.
- Overfitting, Underfitting & Regularization — Overfitting occurs when a model learns training data too well, including its noise, and fails on unseen data.
Unsupervised Learning
- K-Means Clustering: Unsupervised Learning — K-Means groups unlabeled data into k clusters.
Deep Learning
- Intro to Neural Networks: Perceptrons & Layers — A neural network mimics the brain: layers of connected neurons (nodes) process data.
Practical ML
- Practical ML: Avoiding Data Leakage & Deployment — Data leakage is a subtle bug: information from test/validation set leaks into training, inflating performance metrics.
Who this Machine Learning course is for
School and college students in India starting from zero, and anyone revising Machine Learning for placements, board practicals or a project. There is no prerequisite beyond being able to type — the first lessons assume no programming background at all.
How to study this course
- Read the lesson, then run the example in the built-in editor before moving on — reading code is not the same as writing it.
- Try the practice task at the end of each lesson. Getting it wrong and fixing it is where the learning happens.
- When you are stuck, ask the free AI Tutor to explain that specific line rather than skipping ahead.
🤖 Stuck on any of these? Ask Syllab's free AI Tutor to explain step by step →