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

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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?
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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
• Basics of mathematics required for Machine Learning
• Master in the Machine Learning with no prerequisites
• 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 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)

### KUSHAL SHARMA

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

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