Machine Learning Fundamentals

Program demonstrativ 2026 Tehnologie Intermediar en 420 min

Learn how machines learn: from the core ideas of supervised and unsupervised learning, through regression, classification and decision trees, to neural networks and backpropagation — taught with the best free lessons from StatQuest, 3Blue1Brown, Andrej Karpathy and freeCodeCamp, plus quizzes and a hands-on capstone project.

Machine learning powers the products you use every day — spam filters, recommendations, voice assistants, medical diagnostics. This course gives you a solid, intuition-first foundation in how it all works, without requiring a PhD in mathematics. We pair carefully written lessons with the most respected free video education on the internet: StatQuest's crystal-clear statistics, 3Blue1Brown's visual deep-learning series, Andrej Karpathy's code-first neural network walkthroughs, and freeCodeCamp's hands-on projects.

What you will learn

  • What machine learning actually is, and how it differs from traditional programming
  • The three learning paradigms: supervised, unsupervised and reinforcement learning
  • The end-to-end ML workflow: data, training, validation, deployment
  • Core algorithms: linear regression, logistic regression, decision trees and random forests
  • Neural networks from first principles: neurons, gradient descent and backpropagation
  • How to evaluate models honestly: bias vs. variance, cross-validation, the confusion matrix

Who this course is for

Curious beginners with some comfort around basic math (functions, graphs, a little algebra). No prior ML experience is required. By the end you will be able to read ML discussions critically, explain how models learn, and run your own first end-to-end project.

Format: 4 modules, 13 lessons, 2 lesson quizzes and a final exam. Every lesson combines a written explanation with a hand-picked video from a world-class educator.

Structura cursului

13 lecții
01 Foundations: What Machine Learning Really Is 3 lecții · 46 min
  • What Is Machine Learning? 18 min
  • Supervised, Unsupervised and Reinforcement Learning 18 min
  • Test Types of Learning — Quick Check
  • The Machine Learning Workflow: From Data to Deployment 10 min
02 Core Algorithms: Regression, Classification and Trees 3 lecții · 54 min
  • Linear Regression: Predicting Numbers 18 min
  • Logistic Regression: Predicting Categories 18 min
  • Decision Trees and Random Forests 18 min
03 Neural Networks and Deep Learning 4 lecții · 72 min
  • But What Is a Neural Network? 18 min
  • Test Neural Network Basics — Quick Check
  • Gradient Descent: How Networks Learn 18 min
  • Backpropagation: The Algorithm Behind Deep Learning 18 min
  • Building a Neural Network from Scratch with Andrej Karpathy 18 min
04 Evaluating Models and Real-World Practice 3 lecții · 66 min
  • Overfitting, Bias and Variance 18 min
  • Honest Evaluation: Cross-Validation and the Confusion Matrix 18 min
  • Capstone: Your First End-to-End Machine Learning Project 30 min
Evaluare finală
  • Test Machine Learning Fundamentals — Final Exam
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