The Complete Machine Learning Course

  • 90 ultra-detailed ML videos 
  • 12 Hours of learning material
  • Certificate on course completion
  • 3-Days money-back guarantee

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208 learners enrolled

Language: Hindi

Instructors: Kushal Sharma (Former Data Scientist)

Validity Period: 90 days

₹25000 16% OFF



Some videos form the course

Why you should enroll in this course? 

Begin your ML journey by learning the most basic concepts of maths and algorithms!

What you'll learn

  • Implement the Machine Learning algorithms right from scratch
  • How mathematics can help you land a Data Scientist job
  • Basics of mathematics required for Machine Learning
  • Master in the Machine Learning with no prerequisites
  • Implement and tweak the models to get the best accuracy
  • Get a grasp on analytical skills with ML, Mathematics, Statistics, and Python
  • Master the Python for implementing ML algorithms
  • How "Data pre-processing" and "Feature selection" will help you to select the best algorithm for your ML model
  • Advanced algorithms, such as PCA, with the mathematics and logic behind it.
  • Develop perfect intuition of mathematics behind Machine Learning
  • Become an exceptional ML expert

Course Curriculum

Introduction to this course
Why Machine Learning from scratch Preview
What is Machine Learning? Preview
How to get max out of this course Preview
Univariate Linear Regression
1. Introduction Preview
2 Equation Of a Line Preview
3 Linear Regression Explanation Preview
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
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)



  • Former Data Scientist
  • Passive AI Researcher
  • Corporate Trainer
  • Consultancy in AI
  • Educator AI | ML | DS | DL
  • Workshops

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