A Comprehensive Course In Logistic And Linear Regression

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

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

Requirements

Basic Maths

Description

A COMPREHENSIVE COURSE IN LOGISTIC AND LINEAR REGRESSION  IS SET UP TO MAKE LEARNING FUN AND EASYThis 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, StringsPandas and NumpyLinear Algebra - Understanding what is a point and equation of a line. What is a Vector and Vector operationsWhat is a Matrix and Matrix operationsIn depth mathematics behind Logistic RegressionDonors Choose case studyIn depth mathematics behind Linear RegressionAND 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 courseFriendly support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guarantee

Overview

Section 1: Basic Python for Data Analysis (Optional)

Lecture 1 Keywords, Identifiers and Variables

Lecture 2 Variable Assignment

Lecture 3 Strings & List

Lecture 4 Tuple

Lecture 5 Set

Lecture 6 Dictionary

Lecture 7 Data type conversion

Lecture 8 Python Comments

Lecture 9 Print Statement

Lecture 10 Python Arithmetic and Logical Operators

Lecture 11 Identity & Membership Operators

Lecture 12 For & While loop

Lecture 13 Conditional Statement

Lecture 14 Functions

Lecture 15 Modules

Lecture 16 List - Part 1

Lecture 17 List - Part 2

Lecture 18 List - Part 3

Lecture 19 List - Part 4

Lecture 20 List - Part 5

Lecture 21 Tuple - Part 1

Lecture 22 Tuple - Part 2

Lecture 23 Set - Part 1

Lecture 24 Set - Part 2

Lecture 25 Set - Part 3

Lecture 26 Dictionary

Lecture 27 Strings

Lecture 28 Numpy Introduction

Lecture 29 Creating arrays

Lecture 30 Array Operations - Part 1

Lecture 31 Array Masking

Lecture 32 Array Operations - Part 2

Lecture 33 Array Operations - Part 3

Lecture 34 Array broadcasting

Lecture 35 Array - Shape Manipulation & Sorting

Lecture 36 Pandas - Introduction

Lecture 37 Creating a DataFrame

Lecture 38 Accessing elements in a DataFrame

Lecture 39 DataFrame Filtering

Lecture 40 DataFrame Operations

Section 2: Linear Algebra

Lecture 41 Introduction to Linear Equations

Lecture 42 Application of Linear Algebra

Lecture 43 What is a scaler

Lecture 44 What is a point and distance between 2 points

Lecture 45 What is a vector

Lecture 46 Row and Column Vector

Lecture 47 Transpose of a Matrix

Lecture 48 Unit Vector

Lecture 49 Vector Addition and Subtraction

Lecture 50 Inverse of a vector

Lecture 51 Dot Product between two vectors

Lecture 52 Multiplication of a vector with a scaler

Lecture 53 Angle between 2 vectors - Part 1

Lecture 54 Angle between 2 vectors - Part 2

Lecture 55 Orthogonal Vectors

Lecture 56 Orthonormal vectors

Lecture 57 Equation of a line - Part 1

Lecture 58 Equation of a line - Part 2

Lecture 59 Equation of a line - Part 3

Lecture 60 Equation of a line - Part 4

Lecture 61 Projection of a point on a line

Lecture 62 Distance of a point from a line

Lecture 63 How to determine point on the negative and positive side of a line

Lecture 64 Matrix Introduction

Lecture 65 Matrix Operations

Lecture 66 Symmetric, Square, Identity and Diagonal Matrix

Lecture 67 Orthogonal Matrix

Lecture 68 Minor, Cofactor and Determinant of a Matrix (Optional)

Lecture 69 Inverse of a matrix (Optional)

Section 3: Logistic Regression Theory

Lecture 70 LR Introduction

Lecture 71 Geometric Interpretation - Understanding the Nomenclature

Lecture 72 Optimization Equation

Lecture 73 Impact of outliers on the Optimization Equation

Lecture 74 Probabilistic Interpretation of LR at prediction time

Lecture 75 Why taking log doesn't impact the Optimization problem

Lecture 76 Final Optimization Equation

Lecture 77 Regularization

Lecture 78 How to find the class of a new point

Lecture 79 Bais Variance tradeoff

Lecture 80 L1 and L2 Regularization

Lecture 81 Decision Surface

Lecture 82 Elastic Net

Lecture 83 Feature Importance & Interpretability

Lecture 84 Impact of Unbalanced dataset

Lecture 85 Need for data standardization

Lecture 86 Time & Space Complexity

Lecture 87 Similarity Matrix and LR

Lecture 88 Impact of large dimensionality

Lecture 89 Multiclass classification

Lecture 90 Probabilistic Interpretation

Lecture 91 Loss Interpretation of LR

Section 4: Donors Choose

Lecture 92 Donors Choose - Introduction

Lecture 93 Data Understanding

Lecture 94 Data Defintion

Lecture 95 Understanding basics data statistics

Lecture 96 Univariate Analysis - Part 1

Lecture 97 Univariate Analysis - Part 2

Lecture 98 Univariate Analysis - Part 3

Lecture 99 Univariate Analysis - Part 4

Lecture 100 Univariate Analysis - Part 5

Lecture 101 Bag of words

Lecture 102 Term Frequency

Lecture 103 Term Frequency - Inverse Document Frequency

Lecture 104 Word2Vec

Lecture 105 Text Processing

Lecture 106 Train Test Split

Lecture 107 How is vectorization done for categorical data

Lecture 108 Vectorizing Categorical Data

Lecture 109 BOW for Text Data

Lecture 110 Tfidf for Text Data

Lecture 111 W2V for Text Data

Section 5: Linear Regression

Lecture 112 Linear Regression - Introduction

Lecture 113 Intuition

Lecture 114 Loss function

Lecture 115 LR through example

Lecture 116 R square

Lecture 117 Standard deviation and variation

Lecture 118 Covariance

Lecture 119 Corrrelation

Lecture 120 R square and coefficient of correlation(r)

Lecture 121 Why MSE

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