Statistical Learning with Python
5.0
(2)
23 learners
What you'll learn
This course includes
- 20.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 108 lessons • 20.3 hours of video
Statistical Learning with Python
108 lessons
• 20.3 hours
Statistical Learning with Python
108 lessons
• 20.3 hours
- Statistical Learning: 1.1 Opening Remarks18:19
- Statistical Learning: 8 Years Later (Second Edition of the Course)02:19
- Statistical Learning I Introducing Jonathan - Third Edition of the Course I 202301:48
- Statistical Learning: 1.2 Examples and Framework12:13
- Statistical Learning: 2.1 Introduction to Regression Models11:42
- Statistical Learning: 2.2 Dimensionality and Structured Models11:41
- Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff10:05
- Statistical Learning: 2.4 Classification15:38
- Statistical Learning: 2.Py Setting Up Python I 202305:12
- Statistical Learning: 2.Py Data Types, Arrays, and Basics I 202316:42
- Statistical Learning: 2.Py.3 Graphics I 202306:07
- Statistical Learning: 2.Py Indexing and Dataframes I 202310:20
- Statistical Learning: 3.1 Simple linear regression13:02
- Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals08:25
- Statistical Learning: 3.3 Multiple Linear Regression15:38
- Statistical Learning: 3.4 Some important questions14:52
- Statistical Learning: 3.5 Extensions of the Linear Model14:17
- Statistical Learning: 3.Py Linear Regression and statsmodels Package I 202309:10
- Statistical Learning: 3.Py Multiple Linear Regression Package I 202302:26
- Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 202306:33
- Statistical Learning: 4.1 Introduction to Classification Problems10:26
- Statistical Learning: 4.2 Logistic Regression09:08
- Statistical Learning: 4.3 Multivariate Logistic Regression09:54
- Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass07:29
- Statistical Learning: 4.5 Discriminant Analysis07:13
- Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)07:38
- Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)17:43
- Statistical Learning: 4.8 Generalized Linear Models09:35
- Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes10:08
- Statistical Learning: 4.Py Logistic Regression I 202311:44
- Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 202309:58
- Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 202307:06
- Statistical Learning: 5.1 Cross Validation14:02
- Statistical Learning: 5.2 K-fold Cross Validation13:34
- Statistical Learning: 5.3 Cross Validation the wrong and right way10:08
- Statistical Learning: 5.4 The Bootstrap11:30
- Statistical Learning: 5.5 More on the Bootstrap14:36
- Statistical Learning: 5.Py Cross-Validation I 202310:00
- Statistical Learning: 5.Py Bootstrap I 202305:34
- Statistical Learning: 6.1 Introduction and Best Subset Selection13:45
- Statistical Learning: 6.2 Stepwise Selection12:27
- Statistical Learning: 6.3 Backward stepwise selection05:27
- Statistical Learning: 6.4 Estimating test error14:07
- Statistical Learning: 6.5 Validation and cross validation08:44
- Statistical Learning: 6.6 Shrinkage methods and ridge regression12:38
- Statistical Learning: 6.7 The Lasso15:22
- Statistical Learning: 6.8 Tuning parameter selection05:28
- Statistical Learning: 6.9 Dimension Reduction Methods04:46
- Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares15:49
- Statistical Learning: 6.Py Stepwise Regression I 202314:06
- Statistical Learning: 6.Py Ridge Regression and the Lasso I 202319:09
- Statistical Learning: 7.1 Polynomials and Step Functions15:00
- Statistical Learning: 7.2 Piecewise Polynomials and Splines13:14
- Statistical Learning: 7.3 Smoothing Splines10:11
- Statistical Learning: 7.4 Generalized Additive Models and Local Regression10:46
- Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 202308:19
- Statistical Learning: 7.Py Splines I 202303:12
- Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 202308:15
- Statistical Learning: 8.1 Tree based methods14:38
- Statistical Learning: 8.2 More details on Trees11:46
- Statistical Learning: 8.3 Classification Trees11:01
- Statistical Learning: 8.4 Bagging13:46
- Statistical Learning: 8.5 Boosting12:03
- Statistical Learning: 8.6 Bayesian Additive Regression Trees11:34
- Statistical Learning: 8.Py Tree-Based Methods I 202315:30
- Statistical Learning: 9.1 Optimal Separating Hyperplane11:36
- Statistical Learning: 9.2.Support Vector Classifier08:05
- Statistical Learning: 9.3 Feature Expansion and the SVM15:05
- Statistical Learning: 9.4 Example and Comparison with Logistic Regression14:48
- Statistical Learning: 9.Py Support Vector Machines I 202318:58
- Statistical Learning: 9.Py ROC Curves I 202304:45
- Statistical Learning: 10.1 Introduction to Neural Networks15:31
- Statistical Learning: 10.2 Convolutional Neural Networks17:09
- Statistical Learning: 10.3 Document Classification07:47
- Statistical Learning: 10.4 Recurrent Neural Networks14:45
- Statistical Learning: 10.5 Time Series Forecasting16:51
- Statistical Learning: 10.6 Fitting Neural Networks17:04
- Statistical Learning: 10.7 Interpolation and Double Descent11:12
- Statistical Learning: 10.Py Single Layer Model: Hitters Data I 202317:12
- Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 202307:49
- Statistical Learning: 10.Py Convolutional Neural Network: CIFAR Image Data I 202310:13
- Statistical Learning: 10.Py Document Classification and Recurrent Neural Networks I 202309:31
- Statistical Learning: 11.1 Introduction to Survival Data and Censoring14:11
- Statistical Learning: 11.2 Proportional Hazards Model14:32
- Statistical Learning: 11.3 Estimation of Cox Model with Examples13:43
- Statistical Learning: 11.4 Model Evaluation and Further Topics06:13
- Statistical Learning: 11.Py Cox Model: Brain Cancer Data I 202316:52
- Statistical Learning: 11.Py Cox Model: Publication Data I 202304:59
- Statistical Learning: 12.1 Principal Components12:37
- Statistical Learning: 12.2 Higher order principal components17:40
- Statistical Learning: 12.3 k means Clustering17:18
- Statistical Learning: 12.4 Hierarchical Clustering14:46
- Statistical Learning: 12.5 Matrix Completion15:52
- Statistical Learning: 12.6 Breast Cancer Example09:25
- Statistical Learning: 12.Py Principal Components I 202311:27
- Statistical Learning: 12.Py Clustering I 202311:22
- Statistical Learning: 12.Py Application: NCI60 Data I 202312:26
- Statistical Learning: 13.1 Introduction to Hypothesis Testing14:31
- Statistical Learning: 13.1 Introduction to Hypothesis Testing II10:18
- Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate12:31
- Statistical Learning: 13.3 Bonferroni Method for Controlling FWER06:32
- Statistical Learning: 13.4 Holm's Method for Controlling FWER05:57
- Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method11:14
- Statistical Learning: 13.6 Resampling Approaches03:21
- Statistical Learning: 13.6 Resampling Approaches II07:44
- Statistical Learning: 13.Py Multiple Testing I 202317:01
- Statistical Learning: 13.Py False Discovery Rate I 202306:09
- Statistical Learning: 13.Py Multiple Testing and Resampling I 202306:12
