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Machine Learning Tutorial

40 topics · Free · Certificate on completion

Learn Machine Learning — from data and features to models, evaluation and deployment.

Your learning path

Connect features, training, prediction, and evaluation before exploring individual algorithms. Separate what the model learns from how its performance is measured.

Put the lessons into practice

Keep evaluation data separate, fit preprocessing only on training data, and compare against a simple baseline. Explain what the chosen metric misses and avoid interpreting predictions as causation.

What you'll learn

01.What is Machine Learning?02.Types of Machine Learning 03.Data & Features 04.Training vs Test Data 05.Overfitting & Underfitting 06.Bias vs Variance 07.scikit-learn Introduction 08.Linear Regression 09.Multiple Linear Regression 10.Polynomial Regression 11.Logistic Regression 12.K-Nearest Neighbors (KNN)13.Decision Trees 14.Random Forest 15.Support Vector Machines (SVM)16.Naive Bayes 17.K-Means Clustering 18.Hierarchical Clustering 19.PCA (Dimensionality Reduction)20.Feature Scaling 21.Handling Missing Data 22.Encoding Categorical Data 23.Cross-Validation 24.Hyperparameter Tuning 25.Confusion Matrix 26.Accuracy, Precision, Recall, F1 27.ROC Curve & AUC 28.Regression Metrics 29.Regularization (L1 / L2)30.Gradient Descent 31.Neural Networks Basics 32.Activation Functions 33.Loss Functions 34.Ensemble Methods 35.Pipelines 36.Saving & Loading Models 37.NLP Basics 38.Image Basics 39.Deploying an ML Model 40.Ethics & Responsibility