Statistical Learning with Python

Stanford University via YouTube

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Learn supervised and unsupervised machine learning methods through this introductory-level course from Stanford University that emphasizes practical implementation over heavy mathematics. Master essential statistical learning techniques including linear and polynomial regression, logistic regression, and linear discriminant analysis, then advance to cross-validation, bootstrap methods, and model selection using ridge and lasso regularization. Explore nonlinear approaches through splines and generalized additive models before diving into tree-based methods, random forests, and boosting algorithms. Develop expertise in support vector machines, neural networks, and deep learning architectures including convolutional and recurrent networks. Study survival analysis models and tackle unsupervised learning through principal component analysis and clustering techniques using k-means and hierarchical methods. Address multiple testing challenges and learn proper statistical inference procedures. Gain hands-on Python programming skills through dedicated tutorials that progress from basic data manipulation to implementing sophisticated machine learning algorithms using modern data science libraries. Apply theoretical concepts to real-world datasets while following the comprehensive curriculum based on "An Introduction to Statistical Learning, with Applications in Python" textbook, with practical sessions covering everything from simple regression to complex neural network architectures for image classification and time series forecasting.

Syllabus

Statistical Learning: 1.1 Opening Remarks
Statistical Learning: 8 Years Later (Second Edition of the Course)
Statistical Learning I Introducing Jonathan - Third Edition of the Course I 2023
Statistical Learning: 1.2 Examples and Framework
Statistical Learning: 2.1 Introduction to Regression Models
Statistical Learning: 2.2 Dimensionality and Structured Models
Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
Statistical Learning: 2.4 Classification
Statistical Learning: 2.Py Setting Up Python I 2023
Statistical Learning: 2.Py Data Types, Arrays, and Basics I 2023
Statistical Learning: 2.Py.3 Graphics I 2023
Statistical Learning: 2.Py Indexing and Dataframes I 2023
Statistical Learning: 3.1 Simple linear regression
Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
Statistical Learning: 3.3 Multiple Linear Regression
Statistical Learning: 3.4 Some important questions
Statistical Learning: 3.5 Extensions of the Linear Model
Statistical Learning: 3.Py Linear Regression and statsmodels Package I 2023
Statistical Learning: 3.Py Multiple Linear Regression Package I 2023
Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023
Statistical Learning: 4.1 Introduction to Classification Problems
Statistical Learning: 4.2 Logistic Regression
Statistical Learning: 4.3 Multivariate Logistic Regression
Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
Statistical Learning: 4.5 Discriminant Analysis
Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
Statistical Learning: 4.8 Generalized Linear Models
Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
Statistical Learning: 4.Py Logistic Regression I 2023
Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 2023
Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 2023
Statistical Learning: 5.1 Cross Validation
Statistical Learning: 5.2 K-fold Cross Validation
Statistical Learning: 5.3 Cross Validation the wrong and right way
Statistical Learning: 5.4 The Bootstrap
Statistical Learning: 5.5 More on the Bootstrap
Statistical Learning: 5.Py Cross-Validation I 2023
Statistical Learning: 5.Py Bootstrap I 2023
Statistical Learning: 6.1 Introduction and Best Subset Selection
Statistical Learning: 6.2 Stepwise Selection
Statistical Learning: 6.3 Backward stepwise selection
Statistical Learning: 6.4 Estimating test error
Statistical Learning: 6.5 Validation and cross validation
Statistical Learning: 6.6 Shrinkage methods and ridge regression
Statistical Learning: 6.7 The Lasso
Statistical Learning: 6.8 Tuning parameter selection
Statistical Learning: 6.9 Dimension Reduction Methods
Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
Statistical Learning: 6.Py Stepwise Regression I 2023
Statistical Learning: 6.Py Ridge Regression and the Lasso I 2023
Statistical Learning: 7.1 Polynomials and Step Functions
Statistical Learning: 7.2 Piecewise Polynomials and Splines
Statistical Learning: 7.3 Smoothing Splines
Statistical Learning: 7.4 Generalized Additive Models and Local Regression
Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 2023
Statistical Learning: 7.Py Splines I 2023
Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 2023
Statistical Learning: 8.1 Tree based methods
Statistical Learning: 8.2 More details on Trees
Statistical Learning: 8.3 Classification Trees
Statistical Learning: 8.4 Bagging
Statistical Learning: 8.5 Boosting
Statistical Learning: 8.6 Bayesian Additive Regression Trees
Statistical Learning: 8.Py Tree-Based Methods I 2023
Statistical Learning: 9.1 Optimal Separating Hyperplane
Statistical Learning: 9.2.Support Vector Classifier
Statistical Learning: 9.3 Feature Expansion and the SVM
Statistical Learning: 9.4 Example and Comparison with Logistic Regression
Statistical Learning: 9.Py Support Vector Machines I 2023
Statistical Learning: 9.Py ROC Curves I 2023
Statistical Learning: 10.1 Introduction to Neural Networks
Statistical Learning: 10.2 Convolutional Neural Networks
Statistical Learning: 10.3 Document Classification
Statistical Learning: 10.4 Recurrent Neural Networks
Statistical Learning: 10.5 Time Series Forecasting
Statistical Learning: 10.6 Fitting Neural Networks
Statistical Learning: 10.7 Interpolation and Double Descent
Statistical Learning: 10.Py Single Layer Model: Hitters Data I 2023
Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 2023
Statistical Learning: 10.Py Convolutional Neural Network: CIFAR Image Data I 2023
Statistical Learning: 10.Py Document Classification and Recurrent Neural Networks I 2023
Statistical Learning: 11.1 Introduction to Survival Data and Censoring
Statistical Learning: 11.2 Proportional Hazards Model
Statistical Learning: 11.3 Estimation of Cox Model with Examples
Statistical Learning: 11.4 Model Evaluation and Further Topics
Statistical Learning: 11.Py Cox Model: Brain Cancer Data I 2023
Statistical Learning: 11.Py Cox Model: Publication Data I 2023
Statistical Learning: 12.1 Principal Components
Statistical Learning: 12.2 Higher order principal components
Statistical Learning: 12.3 k means Clustering
Statistical Learning: 12.4 Hierarchical Clustering
Statistical Learning: 12.5 Matrix Completion
Statistical Learning: 12.6 Breast Cancer Example
Statistical Learning: 12.Py Principal Components I 2023
Statistical Learning: 12.Py Clustering I 2023
Statistical Learning: 12.Py Application: NCI60 Data I 2023
Statistical Learning: 13.1 Introduction to Hypothesis Testing
Statistical Learning: 13.1 Introduction to Hypothesis Testing II
Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
Statistical Learning: 13.3 Bonferroni Method for Controlling FWER
Statistical Learning: 13.4 Holm's Method for Controlling FWER
Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method
Statistical Learning: 13.6 Resampling Approaches
Statistical Learning: 13.6 Resampling Approaches II
Statistical Learning: 13.Py Multiple Testing I 2023
Statistical Learning: 13.Py False Discovery Rate I 2023
Statistical Learning: 13.Py Multiple Testing and Resampling I 2023