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 Learning12:45
- Machine Learning Fundamentals: Cross Validation06:05
- Machine Learning Fundamentals: The Confusion Matrix07:13
- Machine Learning Fundamentals: Sensitivity and Specificity11:47
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!00:42
- Machine Learning Fundamentals: Bias and Variance06:36
- 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 Regression08:48
- Logistic Regression Details Pt1: Coefficients19:02
- Logistic Regression Details Pt 2: Maximum Likelihood10:23
- Logistic Regression Details Pt 3: R-squared and p-value15:25
- Saturated Models and Deviance18:40
- Logistic Regression in R, Clearly Explained!!!!17:15
- Deviance Residuals06:18
- ROC and AUC, Clearly Explained!16:17
- ROC and AUC in R15:13
- Regularization Part 1: Ridge (L2) Regression20:27
- Regularization Part 2: Lasso (L1) Regression08:19
- Ridge vs Lasso Regression, Visualized!!!09:06
- Regularization Part 3: Elastic Net Regression05:19
- Ridge, Lasso and Elastic-Net Regression in R17:51
- StatQuest: Principal Component Analysis (PCA), Step-by-Step21:58
- StatQuest: PCA main ideas in only 5 minutes!!!06:05
- StatQuest: PCA - Practical Tips08:20
- StatQuest: PCA in R08:57
- StatQuest: PCA in Python11:37
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15:12
- Bam!!! Clearly Explained!!!02:49
- StatQuest: MDS and PCoA08:18
- StatQuest: MDS and PCoA in R07:45
- StatQuest: t-SNE, Clearly Explained11:48
- StatQuest: Hierarchical Clustering11:19
- StatQuest: K-means clustering08:31
- Clustering with DBSCAN, Clearly Explained!!!09:30
- StatQuest: K-nearest neighbors, Clearly Explained05:30
- 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 Data05:16
- 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 Finish01:06:24
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating09:54
- StatQuest: Random Forests in R15:10
- Stochastic Gradient Descent, Clearly Explained!!!10:53
- AdaBoost, Clearly Explained20:54
- Gradient Boost Part 1 (of 4): Regression Main Ideas15:52
- Gradient Boost Part 2 (of 4): Regression Details26:46
- Gradient Boost Part 3 (of 4): Classification17:03
- Gradient Boost Part 4 (of 4): Classification Details37:00
- Troll 2, Clearly Explained!!!05:06
- XGBoost Part 1 (of 4): Regression25:46
- XGBoost Part 2 (of 4): Classification25:18
- XGBoost Part 3 (of 4): Mathematical Details27:24
- XGBoost Part 4 (of 4): Crazy Cool Optimizations24:27
- XGBoost in Python from Start to Finish56:43
- CatBoost Part 1: Ordered Target Encoding08:32
- CatBoost Part 2: Building and Using Trees16:16
- 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 Concepts18:13
