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| Item Details | Price | ||
|---|---|---|---|
5.0 (5 ratings)
208 learners enrolled
Language: Hindi
Instructors: Kushal Sharma (Former Data Scientist)
Validity Period: 90 days
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
| 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 | |||
| 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) | |||
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