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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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