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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.

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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