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    SpicyMags.xyz

    A Comprehensive Course In Logistic And Linear Regression

    Posted By: ELK1nG
    A Comprehensive Course In Logistic And Linear Regression

    A Comprehensive Course In Logistic And Linear Regression
    Published 4/2023
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 8.21 GB | Duration: 19h 22m

    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