A Comprehensive Course In Logistic And Linear Regression – Download Free

Free Download 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

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