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Logistic Regression Details Pt1: Coefficients
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Classroom Contents
Machine Learning
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- 1 A Gentle Introduction to Machine Learning
- 2 Machine Learning Fundamentals: Cross Validation
- 3 Machine Learning Fundamentals: The Confusion Matrix
- 4 Machine Learning Fundamentals: Sensitivity and Specificity
- 5 Machine Learning Fundamentals: Bias and Variance
- 6 Entropy (for data science) Clearly Explained!!!
- 7 The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
- 8 Linear Regression, Clearly Explained!!!
- 9 Multiple Regression, Clearly Explained!!!
- 10 Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
- 11 Design Matrices For Linear Models, Clearly Explained!!!
- 12 ROC and AUC, Clearly Explained!
- 13 ROC and AUC in R
- 14 Odds and Log(Odds), Clearly Explained!!!
- 15 Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
- 16 StatQuest: Logistic Regression
- 17 Logistic Regression Details Pt1: Coefficients
- 18 Logistic Regression Details Pt 2: Maximum Likelihood
- 19 Logistic Regression Details Pt 3: R-squared and p-value
- 20 Saturated Models and Deviance
- 21 Logistic Regression in R, Clearly Explained!!!!
- 22 Deviance Residuals
- 23 Regularization Part 1: Ridge (L2) Regression
- 24 Regularization Part 2: Lasso (L1) Regression
- 25 Ridge vs Lasso Regression, Visualized!!!
- 26 Regularization Part 3: Elastic Net Regression
- 27 Ridge, Lasso and Elastic-Net Regression in R
- 28 StatQuest: Principal Component Analysis (PCA), Step-by-Step
- 29 StatQuest: PCA main ideas in only 5 minutes!!!
- 30 StatQuest: PCA - Practical Tips
- 31 StatQuest: PCA in R
- 32 StatQuest: PCA in Python
- 33 StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
- 34 Bam!!! Clearly Explained!!!
- 35 StatQuest: MDS and PCoA
- 36 StatQuest: MDS and PCoA in R
- 37 StatQuest: t-SNE, Clearly Explained
- 38 StatQuest: Hierarchical Clustering
- 39 StatQuest: K-means clustering
- 40 StatQuest: K-nearest neighbors, Clearly Explained
- 41 Naive Bayes, Clearly Explained!!!
- 42 Gaussian Naive Bayes, Clearly Explained!!!
- 43 Decision and Classification Trees, Clearly Explained!!!
- 44 StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
- 45 Regression Trees, Clearly Explained!!!
- 46 How to Prune Regression Trees, Clearly Explained!!!
- 47 Classification Trees in Python from Start to Finish
- 48 StatQuest: Random Forests Part 1 - Building, Using and Evaluating
- 49 StatQuest: Random Forests Part 2: Missing data and clustering
- 50 StatQuest: Random Forests in R
- 51 The Chain Rule
- 52 Gradient Descent, Step-by-Step
- 53 Stochastic Gradient Descent, Clearly Explained!!!
- 54 AdaBoost, Clearly Explained
- 55 Gradient Boost Part 1 (of 4): Regression Main Ideas
- 56 Gradient Boost Part 2 (of 4): Regression Details
- 57 Gradient Boost Part 3 (of 4): Classification
- 58 Gradient Boost Part 4 (of 4): Classification Details
- 59 Troll 2, Clearly Explained!!!
- 60 Support Vector Machines Part 1 (of 3): Main Ideas!!!
- 61 Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
- 62 Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
- 63 Support Vector Machines in Python from Start to Finish.
- 64 XGBoost Part 1 (of 4): Regression
- 65 XGBoost Part 2 (of 4): Classification
- 66 XGBoost Part 3 (of 4): Mathematical Details
- 67 XGBoost Part 4 (of 4): Crazy Cool Optimizations
- 68 XGBoost in Python from Start to Finish
- 69 Neural Networks Pt. 1: Inside the Black Box
- 70 Neural Networks Pt. 2: Backpropagation Main Ideas
- 71 Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
- 72 Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
- 73 Neural Networks Pt. 3: ReLU In Action!!!
- 74 Neural Networks Pt. 4: Multiple Inputs and Outputs
- 75 Neural Networks Part 5: ArgMax and SoftMax
- 76 The SoftMax Derivative, Step-by-Step!!!
- 77 Neural Networks Part 6: Cross Entropy
- 78 Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
- 79 Neural Networks Part 8: Image Classification with Convolutional Neural Networks
- 80 Tensors for Neural Networks, Clearly Explained!!!
- 81 Lowess and Loess, Clearly Explained!!!
- 82 Population and Estimated Parameters, Clearly Explained!!!
- 83 Clustering with DBSCAN, Clearly Explained!!!
