pro Plan Tier
Machine Learning Fundamentals
Understand the mathematical concepts behind neural networks, transformers, and regression without code overload.
35 hours duration
50 Modular Chapters
Basic / Intermediate / Advanced Tiers
Course Syllabus Outline
01.Introduction to Machine Learning: What It Is and How It Differs from ProgrammingFREE PREVIEW
02.The Three Types of Machine Learning: Supervised, Unsupervised, and ReinforcementFREE PREVIEW
03.Understanding Datasets: Features, Labels, Training, and Test Sets
04.Linear Regression: Predicting Continuous Values from Data
05.Logistic Regression: Understanding Binary Classification
06.The Bias-Variance Trade-off: Underfitting and Overfitting Explained
07.Decision Trees: How Machines Make Rule-Based Decisions
08.Random Forests: Ensemble Learning and Averaging Predictions
09.K-Nearest Neighbours (KNN): Similarity-Based Classification
10.Support Vector Machines (SVM): Finding the Optimal Decision Boundary
11.Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, and AUC-ROC
12.Data Preprocessing: Handling Missing Values, Scaling, and Encoding
13.Feature Engineering: Creating Informative Inputs for Models
14.Cross-Validation: Reliable Model Performance Estimation
15.Level Review: Core ML Fundamentals Checklist