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Machine Learning with Python
Linear Regression
Introduction and Correlation (3:57)
LAB - Correlation Calculation in Python (6:58)
Beyond Pearson Correlation (2:26)
From Correlation to Regression (6:59)
LAB Regression Line Fitting (7:06)
How good is my line (5:47)
R Squared (3:36)
Multiple Regression model (6:47)
Adjusted R Squared (5:32)
Multiple Regression Issues (9:34)
LAB - Multicollinearity (4:35)
Regression Conclusion (3:00)
Session 1. Linear Regression- Case Study
Logistic Regression
Introduction and Need of Logistic Regression (8:07)
A Logistic Function (4:07)
Building a Logistic Regression Line in Python (7:02)
Multiple Logistic Regression Model (5:38)
Goodness of Fit For Logistic Regression (7:08)
Multicollinearity in Logistic Regression (3:32)
Individual Impact of Variables (3:31)
Model Selection (7:04)
Logistic Regression Conclusion (1:28)
Session 2. Logistic Regression-Case Study
Decision Trees
Introduction to Decision Trees and Segmentation (6:31)
The Decision Tree Philosophy and Approach (14:14)
The Splitting Criterion & Entropy (15:24)
Information Gain Calculation (8:56)
The Decision Tree Algorithm (10:34)
Many Splits for a Variable (5:56)
Decision Tree Fitting and Interpretation (9:51)
Decision Tree Validation (3:13)
Decision Tree Over fitting (7:31)
Pruning and Pruning Parameters (6:48)
LAB - Tree Building and Model Selection p.1 (4:29)
LAB - Tree Building and Model Selection p.2 (6:13)
Conclusion (2:04)
Session 3. Decision Trees-Case Study
Model Selection and Cross Validation
Introduction to Model Selection (2:00)
Sensitivity and Specificity (3:01)
LAB - Sensitivity and Specificity in Python (6:20)
Sensitivity-Specificity contd p.1 (1:43)
Sensitivity-Specificity contd P.2 (9:29)
ROC and AUC (5:43)
LAB - ROC and AUC (2:49)
The Best Model (2:08)
LAB - The Best Model (2:19)
Errors (5:42)
OverFitting and UnderFitting p.1 (5:11)
OverFitting and UnderFitting p.2 (3:09)
OverFitting and UnderFitting p.3 (3:20)
OverFitting and UnderFitting p.4 (1:51)
Bias-Variance Trade off (9:43)
Hold-Out Data Validation (1:58)
LAB - Hold-Out Data Validation (2:41)
Ten-Fold CrossValidation (5:08)
LAB - Ten-Fold CrossValidation (3:02)
Boot-Strap CrossValidation (6:01)
LAB - Boot-Strap CrossValidation (2:09)
MS & CV Conclusion (1:42)
Session 4. Model Selection _ Cross Validation-Case Study
Neural Networks
Neural Networks Introduction (1:46)
LAB - Logistic Regression Recap (4:49)
Decision Boundary - Logistic Regression (4:56)
LAB -Decision Boundary (2:15)
New Representation for Logistic Regression (4:51)
Non-Linear Decision Boundary - Problem (3:15)
Non-Linear Decision Boundary - Solution (7:00)
LAB - Intermediate Output (6:15)
Neural Network Intuition (7:35)
Neural Network Algorithm (6:47)
Demo - Neural Network Angorithm (6:16)
LAB - Neural Network in Python (11:29)
Local Minima and Number of Hidden Layers (5:09)
LAB - Digit Recognizer (13:50)
Conclusion (5:36)
Session 5. Neural Networks-Case Study
SVM - Support Vector Machines
Introduction to SVM (2:01)
The Classifier and Decision boundary (4:12)
LAB - The Classifier and Decision boundary (2:33)
SVM- The Large Margin Classifier (1:34)
The SVM algorithm and Results (3:55)
SVM on Python (4:40)
Non-Linear Boundary (3:38)
Kernal Trick (5:52)
Kernal Trick in Python (5:53)
Soft Margin and Validation (3:45)
SVM Advantages-DisAdvantages and Applications (2:56)
LAB - Digit Recogniser (5:24)
SVM Conclusion (1:08)
Session 6. Support Vector Machines-Case study
Random Forests and Boosting
Introduction to Bagging Random Forests and Boosting (0:50)
Wisdom of Crowd (5:39)
Ensemble Learning (5:34)
Ensemble Models (5:36)
Bagging (6:53)
Random Forests (10:21)
LAB - Random Forests in Python (6:27)
Boosting (6:53)
Boosting Illustration (7:14)
LAB - Boosting in Python (6:01)
Conclusion (3:19)
Session 7. Random Forest and Boosting-Case Study
LAB - Tree Building and Model Selection p.1
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