Preview The Complete Machine Learning Course

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Introduction to this course

Why Machine Learning from scratch

What is Machine Learning?

How to get max out of this course

Univariate Linear Regression

1. Introduction

2 Equation Of a Line

3 Linear Regression Explanation

4 Gradient Descent Optimizer

Calculus Basics (Optional) (Part-1)

Calculus Basics (Optional) (Part-2)

5 Linear Regression Code

Assignment #1: Predict Population

Multivariate Linear Regression

6 Matrices and Vectors Basics

7 Intro Multivariate Linear Regression

8 Gradients(Pattern) of Multivariate Linear Regression

9 Coding Multivariate Linear Regression(Part-1)

10 Coding Multivariate Linear Regression (Basic functions)

11 Coding Multivariate Linear Regression (Derivative Functions)

12 Coding Multivariate Linear Regression (GradientDescentTraining)

Assignment #2 Predict Song release year using song features

Polynomial Regression

13 Polynomial Regression Theory

14 Polynomial Regression(Making curve equation)

15 Coding Polynomial Regression

Predict Atomic Radius of superconductors

Naive Bayes

16 Introduction to Probability(Sample & Population)

16(Bonus) Set Theory

17 Conditional Probablity

18 Multiplication Theorem of Probability

19 Intuition to Naive Bayes

20 Theorem Of Total Probability & Connection to Bayes Theorem

21 Bayes Theorem Proof Complete using Multiplication Theorem of Probability

22 Computing Bayes Theorem in terms of Data(Part -1) Basic Relative Frequencies

23 Probability Distribution Functions Explanation for Naive Bayes & Bayes Theorem

24 Computing Bayes Theorem in terms of Data(Part 2) Prior Probabilities Computation

25 Naive Bayes Code

26 Intuition Naive Bayes Code

Logistic Regression

27 Logistic Regression Introduction

28 Sigmoid Function Derivation by odds function and link function by Linear Regression

29 Sigmoid Equation Behaviour Explanation

30 Sigmoid Behaviour with Data

31 Cross Entropy Loss Intuition with graph

32 Deriving Cross Entropy Loss(Introduction)

33 Maximum Likelihood Estimation Introduction to derive cross entropy loss

34 Deriving Cross Entropy Loss Full Calculation after maximum likelihood estimation

35 Derivative Of Sigmoid

36 Derivative of Binary Cross Entropy Loss for Logistic Regression

37 Data Generation for Logistic Regression Corona Virus Patient Classification

38 LR Code Derivatives and Training

Linear Algebra

39 Introduction to Linear Algebra

40 Basics of Vectors and Matrices

41 Vector Space

42 Unit Vectors

43 Span of Unit Vectors

44 Basis Vectors and Span of Basis Vectors

45 Dot Product explained by Projection of Vectors

46 Linear Transformations

47 Determinants by Linear Transformation and Mathematical Way

48 Inverse of a Matrix

49 Linearly Dependent vs independent vectors and Singular vs non singular matrics

50 EigenVectors

51 Eigen Values

52 Intuition about Computing Eigen Values and Eigen Vectors

53 EigenValue Decomposition with Posititive Definite Symmetric Matrix plus Orthogonal and Orthonormal Vectors

Principal Component Analysis

54 Introduction to PCA

55 Basic Statistics

56 Correlation and Covariance Matrices

57 Projection of Vectors

58 Vector in terms of Linear Combination of Basis Vectors of Vector Space

59 Projection of Vector(X) on a Basis Vector by taking Linear Combination of Basis Vectors of that Vector(X)

60 Dot product as Projection of X on all Basis vectors in form of a Matrix

61 Dot product on Orthogonal Basis Vectors and Orthonormal Basis Vectors and explaining the output(Two Cases)

62 Projection of a vector on a rotated vector space

63 PCA(1) Ideal Covariance Matrix and projection to rotated vector space introduction

64 PCA(2) Covariance to Ideal Covariance by EVD

65 PCA(3) Reducing Features and finding optimal data by dot product between Data(X) and Eigen Vector Matrix of Covariance matrix

66 PCA Code

Introduction to Neural Networks

67 Intro to Neural Networks and its importance

68 Feed Forward Neural Networks Intro(Learning Path)

69 Explanation to Neurons and Layers

70 Dot product between layers and weights plus bias

71 Error Function and Introduction to Minimizing Error with Gradient Descent

72 Derivatives using Chain Rule for Neural Networks

73 Whole Summary of Feed Forward Neural Network

75 ANN OneHotEncoding

Convolutional Neural Network

76 Image_Processing_Basics

77 CNN Architecture

78 Coding CNN

79 CNN Maxpooling

Autoencoders

80 Autoencoders Basics & Applications

81 Vanilla Autoencoder Architecture

82 DC Autoencoder Architecture

83 Coding DC Denoising Autoencoder

Generative Adversarial Networks

84 Intro to GAN

85 Vanilla GAN Architecture

86 DC GAN Architecture

87 Coding Vanilla GAN

Introduction to Sequential Models

Sequential Models vs Feed Forward NN Models

Understanding the architecture common things

RNN Theory

Coding RNN: Recurrent Neural Networks

Sequential Models & RNN Basics(Session recording)

RNN BackPropagation and Tensorflow Code(Session Recording)

LSTM Network Architecture (Session Recording)

Preview - The Complete Machine Learning Course