Learn Data Science with 5 Free Books

Despite lot of free resources like youtube, Udemy and other articles, One has to go through a clear structure of study programs.

1. A Whirlwind Tour of Python 2. Think Stats: Probability and Statistics for Programmers 3. Introduction to Linear Algebra for Applied Machine Learning with Python 4. Introduction to Machine Learning with Python 5. Deep Learning with Python

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1. A Whirlwind Tour of Python

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2.Think Stats: Probability and Statistics for Programmers

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3. Introduction to Linear Algebra for Applied Machine Learning with Python

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4. Introduction to Machine Learning with Python

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5. Deep Learning with Python

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Learn Data Science with 5 Free Books

By Rickey October 18, 2023

Despite lot of free resources like youtube, Udemy and other articles, One has to go through a clear structure of study programs.

1. A Whirlwind Tour of Python 2. Think Stats: Probability and Statistics for Programmers 3. Introduction to Linear Algebra for Applied Machine Learning with Python 4. Introduction to Machine Learning with Python 5. Deep Learning with Python

1. A Whirlwind Tour of Python 2. Think Stats: Probability and Statistics for Programmers 3. Introduction to Linear Algebra for Applied Machine Learning with Python 4. Introduction to Machine Learning with Python 5. Deep Learning with Python

1. A Whirlwind Tour of Python

1. A Whirlwind Tour of Python

1. Basic Syntax 2. Variables 3. Operators 4. Principal Data Types 5. For Loop 6. lWhile loop 7. Functions 8. If-elif-else 9. Fast overview of Python libraries

2.Think Stats: Probability and Statistics for Programmers

2.Think Stats: Probability and Statistics for Programmers

1. · Summary Statistics 2. · Data Distribution 3. · Probability Distributions 4. · Bayes’s Theorem 5. · Central limit theorem 6. · Hypothesis testing 7. · Estimation

3. Introduction to Linear Algebra for Applied Machine Learning with Python

3. Introduction to Linear Algebra for Applied Machine Learning with Python

1. ·Vectors 2. Matrices 3. · Projections 4. ·Determinant 5. · ·Eigenvectors and Eigenvalues 6. · Singular Value Decomposition

4. Introduction to Machine Learning with Python

4. Introduction to Machine Learning with Python

1. · Linear Regression 2. ·Naïve Bayes 3. Decision Trees 4. Ensembles of Decision Trees 5. ·Support Vector Machines 6. · Principal Component Analysis 7. · t-SNE 8. · K-Means Clustering 9. · DBSCAN

5. Deep Learning with Python

5. Deep Learning with Python

1. Neural Networks 2. Convolutional Neural Networks 3. Recurrent Neural Networks 4. · LSTM 5. · Generative Adversarial Networks

1. Neural Networks 2. Convolutional Neural Networks 3. Recurrent Neural Networks 4. · LSTM 5. · Generative Adversarial Networks

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