Machine Learning Fundamentals
Master the core concepts of machine learning with hands-on lessons covering algorithms, data preprocessing, model evaluation, and deployment strategies.
Course Progress
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Introduction to Machine Learning
45 min • Fundamental concepts, types of ML algorithms, and real-world applications
Data Preprocessing & Feature Engineering
60 min • Data cleaning, transformation, and feature selection techniques
Linear Regression & Classification
55 min • Linear models, logistic regression, and regularization techniques
Decision Trees & Random Forests
50 min • Tree-based algorithms, ensemble methods, and feature importance
Support Vector Machines
55 min • SVM theory, kernel methods, and optimization techniques
Clustering & Unsupervised Learning
60 min • K-means, hierarchical clustering, PCA, and dimensionality reduction
Neural Networks Basics
65 min • Perceptrons, multi-layer networks, and backpropagation algorithm
Model Evaluation & Validation
50 min • Performance metrics, cross-validation, and bias-variance tradeoff
Hyperparameter Tuning & Optimization
55 min • Grid search, random search, and Bayesian optimization techniques
ML Project Lifecycle & Best Practices
70 min • End-to-end project management, deployment, and monitoring