Classification: Logistic Regression

What you'll be able to do

• What is logistic regression and where can it be used?
• Build the classification model from scratch
• Perform the pre model built analysis
• Check if model is overfitted and learn how to overcome the problem of overfitting
• How to use logistic regression for multi class classification
• Evaluate the model using various evaluation metrices
• Implement logistic regression in a project and solve it end to end

1. 01Section 1: Logistic Regression - Level 16 lessons · 50 min

   1. Introduction to Logistic RegressionPreview11:36
   2. Download Resources1:00
   3. Motivation behind Logistic RegressionPreview10:33
   4. Logistic Regression Theory 1 - SigmoidPreview7:00
   5. Logistic Regression Theory 2 - Log OddsPreview12:04
   6. Log Odds vs Sigmoid SquashingPreview8:15
2. 02Section 2: Logistic Regression Theory - Level 25 lessons · 42 min

   1. Maximum Likelihood Estimation - Part 110:00
   2. Maximum Likelihood Estimation - Part 212:32
   3. Logistic Regression via Gradient Descent11:28
   4. Multi Class Logistic Regression - One vs Rest7:09
   5. Course Review1:00
3. 03Section 3: Logistic Regression Concepts - Level 35 lessons · 25 min

   1. Regularized Logistic Regression5:34
   2. Multicollinearity - How it affects inference7:05
   3. Perturbation Technique4:11
   4. Logistic Regression for Non Linearly Separable Data4:11
   5. Feature Engineering Approaches4:11
4. 04Section 4: Examining Model Fit (Project)8 lessons · 1 h 10 min

   1. Problem Statement10:04
   2. EDA11:14
   3. Building Logistic Regression4:01
   4. Perturbation Test Coade Demo17:03
   5. Scikit vs Statsmodels Logit API7:25
   6. McFaddens Pseudo R-Squared7:43
   7. Likelihood Ratio Test7:43
   8. Walds Statistic4:40
5. 05Section 5: Evaluating Performance12 lessons · 1 h 34 min

   1. Predicting on Test Data5:46
   2. Accuracy and caveats6:00
   3. Confusion Matrix, TPR, FPR, TNR, FNR15:14
   4. Precision and Recall with F1 Score6:27
   5. Receiver Operating Characteristic (ROC) Curve12:55
   6. Concordance and Discordance5:20
   7. Cross Entropy8:24
   8. KS Statistic and Gains Table15:10
   9. Hyper Parameter Tuning9:11
   10. Evaluation Metrics Demo2:23
   11. Review Questions6:00
   12. Assignment1:00

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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