Logistic Regression: Binary Classification

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In this video, we will discuss our first classification algorithm called Logistic Regression.

Contents

1: Welcome to Machine Learning Experience

1.1: AI Family Tree

1.2: Course Overview

2: Machine Learning Basics

2.1: Who is Peter Pandey?

2.2: Peter Pandey’s journey to learn Machine Learning

2.3: What is Machine Learning?

2.4: Classification vs Regression

2.5: Supervised vs Unsupervised Learning

3: Supervised Machine Learning: Regression

3.1: Simple Linear Regression

3.2: Multiple Linear Regression

3.3: Quiz

3.4: Exercise

3.5: Cost Function

3.6: Derivatives and Partial Derivatives

3.7: Chain Rule

3.8: Quiz

3.9: Exercise

3.10: Gradient Descent Theory

3.11: Gradient Descent: Python Implementation

3.12: Why MSE (and not MAE)?

3.13: Model Evaluation: Train, Test Split

3.14: Model Evaluation: Metrics

3.15: Peter Pandey Flexes his ML skills on LinkedIn

3.16: Quiz

3.17: Exercise

3.18: Data Preprocessing: One Hot Encoding

3.19: Quiz

3.20: Polynomial Regression

3.21: Quiz

3.22: Exercise

3.23: Overfitting and Underfitting

3.24: Reasons and Remedies For Overfitting / Underfitting

3.25: L1 and L2 Regularization

3.26: Bias Variance Trade Off

3.27: Quiz

3.28: Exercise

3.29: Chapter Summary

4: Supervised Machine Learning: Classification

4.1: Introduction to Classification

4.2: Logistic Regression: Binary Classification

4.3: Model Evaluation: Accuracy, Precision and Recall

4.4: Quiz

4.5: Exercise

4.6: Model Evaluation: F1 Score, Confusion Matrix

4.7: Logistic Regression: Multiclass Classification

4.8: Cost Function: Log Loss

4.9: Quiz

4.10: Exercise

4.11: Support Vector Machine (SVM)

4.12: Data Pre-processing: Scaling

4.13: Sklearn Pipeline

4.14: Quiz (disabled)

4.15: Quiz

4.16: Exercise

4.17: Naive Bayes: Theory

4.18: Naive Bayes: SMS Spam Classification

4.19: Quiz

4.20: Exercise

4.21: Decision Tree: Theory

4.22: Decision Tree: Salary Classification

4.23: I Need a Favour

4.24: Quiz

4.25: Exercise

4.26: Handle Class Imbalance: Theory

4.27: Handle Class Imbalance Using imblearn: Churn Prediction

4.28: Quiz

4.29: Exercise

4.30: Get inspired by Peter Pandey

4.31: Chapter Summary

5: Ensemble Learning

5.1: What is Ensemble Learning?

5.2: Majority Voting, Average and Weighted Average

5.3: Bagging

5.4: Bagging: Random Forest

5.5: Random Forest: Raisin Classification

5.6: Quiz

5.7: Exercise