Machine Learning

Algorithms that learn from data: regression, classification, trees, ensembles and the theory behind them.

advanced~60 hours17 topics~34h of lessonsRoadmap Level 5

ML turns data into predictions; knowing how models are built and evaluated lets you use them honestly.

RECOMMENDED BEFORE THIS
○ Python Programming○ Probability○ Linear Algebra○ Statistics

These are suggested first. You can still open any topic — this only helps you pick an order.

Prerequisites:Python ProgrammingProbabilityLinear AlgebraStatistics
Learning Path
07 stages
17 topics · 7 stages0/17 completed
01

Fundamentals

Start from zero. Build the vocabulary.

What is Machine Learning?~45 minbeginnerTrain/Test Split and Overfitting~75 minbeginnerLinear Regression~90 minintermediate
02

Core Concepts

The ideas you cannot skip.

Logistic Regression and Classification~75 minintermediateDecision Trees~75 minintermediateEnsembles: Random Forests and Gradient Boosting~90 minintermediatek-Nearest Neighbors and Distance~60 minbeginner
03

Intermediate

Connect ideas and go deeper.

SVMs and Kernels~75 minadvancedClustering: k-Means and Beyond~75 minintermediateDimensionality Reduction with PCA~60 minadvancedRegularization and Bias-Variance~60 minadvanced
04

Advanced

Beyond the basics.

Hyperparameter Tuning and CV~90 minintermediateImbalanced Data and Sampling~60 minadvancedMLOps Basics: Serving and Monitoring~75 minadvanced
05

Practice

Solve problems until it sticks.

Practice: ML Problems~300 minintermediate
06

Projects

Apply everything in real work.

Project: ML Portfolio Project~600 minadvanced
07

Interview Prep

Convert knowledge into offers.

ML Interview Questions~120 minadvanced