Machine Learning
5.0
(2)
38 learners
What you'll learn
This course includes
- 29.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 73 lessons • 29.5 hours of video
Machine Learning
73 lessons
• 18.5 hours
Machine Learning
73 lessons
• 18.5 hours
- A Gentle Introduction to Machine Learning 12:45
- Machine Learning Fundamentals: Cross Validation 06:05
- Machine Learning Fundamentals: The Confusion Matrix 07:13
- Machine Learning Fundamentals: Sensitivity and Specificity 11:47
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!! 00:42
- Machine Learning Fundamentals: Bias and Variance 06:36
- Entropy (for data science) Clearly Explained!!! 16:35
- Mutual Information, Clearly Explained!!! 16:14
- The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.) 09:22
- Linear Regression, Clearly Explained!!! 27:27
- Multiple Regression, Clearly Explained!!! 05:25
- Using Linear Models for t-tests and ANOVA, Clearly Explained!!! 11:38
- Design Matrices For Linear Models, Clearly Explained!!! 14:40
- Odds and Log(Odds), Clearly Explained!!! 11:31
- Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 16:20
- StatQuest: Logistic Regression 08:48
- Logistic Regression Details Pt1: Coefficients 19:02
- Logistic Regression Details Pt 2: Maximum Likelihood 10:23
- Logistic Regression Details Pt 3: R-squared and p-value 15:25
- Saturated Models and Deviance 18:40
- Logistic Regression in R, Clearly Explained!!!! 17:15
- Deviance Residuals 06:18
- ROC and AUC, Clearly Explained! 16:17
- ROC and AUC in R 15:13
- Regularization Part 1: Ridge (L2) Regression 20:27
- Regularization Part 2: Lasso (L1) Regression 08:19
- Ridge vs Lasso Regression, Visualized!!! 09:06
- Regularization Part 3: Elastic Net Regression 05:19
- Ridge, Lasso and Elastic-Net Regression in R 17:51
- StatQuest: Principal Component Analysis (PCA), Step-by-Step 21:58
- StatQuest: PCA main ideas in only 5 minutes!!! 06:05
- StatQuest: PCA - Practical Tips 08:20
- StatQuest: PCA in R 08:57
- StatQuest: PCA in Python 11:37
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 15:12
- Bam!!! Clearly Explained!!! 02:49
- StatQuest: MDS and PCoA 08:18
- StatQuest: MDS and PCoA in R 07:45
- StatQuest: t-SNE, Clearly Explained 11:48
- StatQuest: Hierarchical Clustering 11:19
- StatQuest: K-means clustering 08:31
- Clustering with DBSCAN, Clearly Explained!!! 09:30
- StatQuest: K-nearest neighbors, Clearly Explained 05:30
- Naive Bayes, Clearly Explained!!! 15:12
- Gaussian Naive Bayes, Clearly Explained!!! 09:26
- Decision and Classification Trees, Clearly Explained!!! 18:08
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data 05:16
- Regression Trees, Clearly Explained!!! 22:33
- How to Prune Regression Trees, Clearly Explained!!! 16:15
- One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!! 15:23
- Classification Trees in Python from Start to Finish 01:06:24
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating 09:54
- StatQuest: Random Forests in R 15:10
- Stochastic Gradient Descent, Clearly Explained!!! 10:53
- AdaBoost, Clearly Explained 20:54
- Gradient Boost Part 1 (of 4): Regression Main Ideas 15:52
- Gradient Boost Part 2 (of 4): Regression Details 26:46
- Gradient Boost Part 3 (of 4): Classification 17:03
- Gradient Boost Part 4 (of 4): Classification Details 37:00
- Troll 2, Clearly Explained!!! 05:06
- XGBoost Part 1 (of 4): Regression 25:46
- XGBoost Part 2 (of 4): Classification 25:18
- XGBoost Part 3 (of 4): Mathematical Details 27:24
- XGBoost Part 4 (of 4): Crazy Cool Optimizations 24:27
- XGBoost in Python from Start to Finish 56:43
- CatBoost Part 1: Ordered Target Encoding 08:32
- CatBoost Part 2: Building and Using Trees 16:16
- Cosine Similarity, Clearly Explained!!! 10:14
- Support Vector Machines Part 1 (of 3): Main Ideas!!! 20:32
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3) 07:15
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3) 15:52
- Support Vector Machines in Python from Start to Finish. 44:49
- Reinforcement Learning: Essential Concepts 18:13
Take this course with you
Download the app to continue lessons from your phone anytime.
