A comprehensive course in Logistic and Linear Regression.

Name: A comprehensive course in Logistic and Linear Regression.
Rating: 4.6 (12 reviews)

Understand ML models through first principle,develop mathematical understanding,build intuition & work out case studies

Highest Rated

Created byNewton Academy

Last updated 6/2025

English

What you'll learn

Basics of Python. If you already know Python then this can be skipped.
Linear Algebra to develop mathematical Intuition behind each algorithm.
Mathematics behind Logistic Regression.
Logistic Regression Case Study - Donors Choose
Mathematics behind Linear Regression
Linear Regression Case Study

Course content

5 sections • 121 lectures • 19h 21m total length

Keywords, Identifiers and Variables8:12
Variable Assignment6:53
Strings & List17:13
Tuple3:19
Set4:19
Dictionary5:20
Data type conversion9:07
Python Comments2:47
Print Statement5:40
Python Arithmetic and Logical Operators10:02
Identity & Membership Operators6:05
For & While loop7:03
Conditional Statement2:50
Functions19:10
Modules7:11
List - Part 16:19
List - Part 213:26
List - Part 310:34
List - Part 412:56
List - Part 59:27
Tuple - Part 16:01
Tuple - Part 26:01
Set - Part 15:38
Set - Part 28:11
Set - Part 32:56
Dictionary16:38
Strings11:16
Numpy Introduction8:56
Creating arrays16:54
Array Operations - Part 112:50
Array Masking3:59
Array Operations - Part 29:33
Array Operations - Part 313:10
Array broadcasting6:37
Array - Shape Manipulation & Sorting10:18
Pandas - Introduction15:04
Creating a DataFrame6:12
Accessing elements in a DataFrame12:07
DataFrame Filtering4:32
DataFrame Operations24:50

Introduction to Linear Equations13:36
Application of Linear Algebra7:41
What is a scaler9:38
What is a point and distance between 2 points15:40
What is a vector9:15
Row and Column Vector21:28
Transpose of a Matrix2:35
Unit Vector9:48
Vector Addition and Subtraction10:56
Inverse of a vector2:54
Dot Product between two vectors11:00
Multiplication of a vector with a scaler2:29
Angle between 2 vectors - Part 12:26
Angle between 2 vectors - Part 24:27
Orthogonal Vectors1:42
Orthonormal vectors2:50
Equation of a line - Part 111:04
Equation of a line - Part 25:33
Equation of a line - Part 36:28
Equation of a line - Part 415:57
Projection of a point on a line5:42
Distance of a point from a line24:04
How to determine point on the negative and positive side of a line13:28
Matrix Introduction5:30
Matrix Operations15:07
Symmetric, Square, Identity and Diagonal Matrix9:09
Orthogonal Matrix9:14
Minor, Cofactor and Determinant of a Matrix (Optional)12:04
Inverse of a matrix (Optional)15:22

LR Introduction11:28
Geometric Interpretation - Understanding the Nomenclature13:13
Optimization Equation17:50
Impact of outliers on the Optimization Equation17:35
Probabilistic Interpretation of LR at prediction time7:11
Why taking log doesn't impact the Optimization problem9:09
Final Optimization Equation10:52
Regularization9:44
How to find the class of a new point1:48
Bais Variance tradeoff16:30
L1 and L2 Regularization3:03
Decision Surface1:38
Elastic Net1:43
Feature Importance & Interpretability11:21
Impact of Unbalanced dataset7:52
Need for data standardization5:51
Time & Space Complexity13:45
Similarity Matrix and LR4:29
Impact of large dimensionality1:35
Multiclass classification5:50
Probabilistic Interpretation22:28
Loss Interpretation of LR20:43

Donors Choose - Introduction8:17
Data Understanding6:39
Data Defintion9:16
Understanding basics data statistics13:05
Univariate Analysis - Part 110:48
Univariate Analysis - Part 214:53
Univariate Analysis - Part 310:01
Univariate Analysis - Part 47:57
Univariate Analysis - Part 55:38
Bag of words7:52
Term Frequency6:30
Term Frequency - Inverse Document Frequency8:48
Word2Vec7:50
Text Processing19:02
Train Test Split11:26
How is vectorization done for categorical data5:20
Vectorizing Categorical Data14:20
BOW for Text Data11:00
Tfidf for Text Data5:48
W2V for Text Data15:12

Requirements

Basic Maths

Description

A COMPREHENSIVE COURSE IN LOGISTIC AND LINEAR REGRESSION IS SET UP TO MAKE LEARNING FUN AND EASY

This 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Python, Linear Algebra, Mathematics behind the ML algorithms and case studies. Topic is organized into the following sections:

Python Basics, Data Structures - List, Tuple, Set, Dictionary, Strings
Pandas and Numpy
Linear Algebra - Understanding what is a point and equation of a line.
What is a Vector and Vector operations
What is a Matrix and Matrix operations
In depth mathematics behind Logistic Regression
Donors Choose case study
In depth mathematics behind Linear Regression.

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

We will start with basics and understand the intuition behind each topic.
Video lecture explaining the concept with many real-life examples so that the concept is drilled in.
Walkthrough of worked out examples to see different ways of asking question and solving them.
Logically connected concepts which slowly builds up.

Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.

YOU'LL ALSO GET:

Lifetime access to the course
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee

Who this course is for:

Data Analysts wanting to transition into Data Scientists
Dats Scientists wanting to understand the mathematical rigour behind the algorithms.
Just about anybody who is interested in Machine Learning
Maths enthusiasts

A comprehensive course in Logistic and Linear Regression.

What you'll learn

Explore related topics

Course content

Basic Python for Data Analysis (Optional)40 lectures • 6hr 10min

Linear Algebra29 lectures • 4hr 37min

Logistic Regression Theory22 lectures • 3hr 36min

Donors Choose20 lectures • 3hr 20min

Linear Regression10 lectures • 1hr 40min

Requirements

Description

Who this course is for: