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Amazing Article of Machine Learning

Statistics Article

  1. Different Distribution for Machine learning https://www.analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science/
  2. Your Guide to Master Hypothesis Testing in Statistics https://www.analyticsvidhya.com/blog/2015/09/hypothesis-testing-explained/?utm_source=blog&utm_medium=statistics-t-test-introduction-r-implementation
  3. Statistics for Data Science: Introduction to the Central Limit Theorem https://www.analyticsvidhya.com/blog/2019/05/statistics-101-introduction-central-limit-theorem/
  4. Comprehensive & Practical Inferential Statistics Guide for data science https://www.analyticsvidhya.com/blog/2017/01/comprehensive-practical-guide-inferential-statistics-data-science/
  5. Bayesian Statistics explained to Beginners in Simple English https://www.analyticsvidhya.com/blog/2016/06/bayesian-statistics-beginners-simple-english/
  6. Statistics for Data Science: Introduction to t-test and its Different Types https://www.analyticsvidhya.com/blog/2019/05/statistics-t-test-introduction-r-implementation/

Math Behind Machine Learning, Deep learning, Reinforcement Learning

  1. Introduction of Machine learning (Why,How,What) : https://medium.com/deep-math-machine-learning-ai/introduction-of-machine-learning-why-how-what-84c881c70763

  2. Different types of Machine learning and their types. : https://medium.com/deep-math-machine-learning-ai/different-types-of-machine-learning-and-their-types-34760b9128a2

  3. Chapter 1 :Complete Linear Regression with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-1-complete-linear-regression-with-math-25b2639dde23

  4. Chapter 1.2: Gradient Descent with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-1-2-gradient-descent-with-math-d4f2871af402

  5. Chapter 1.3:Code for linear regression with Gradient descent (from scratch and Tensorflow & Scikit Learn ). https://medium.com/deep-math-machine-learning-ai/chapter-1-3-7ba084ff7e6d

  6. Chapter 2.0 : Logistic Regression with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-2-0-logistic-regression-with-math-e9cbb3ec6077

  7. Chapter 3: Support Vector machine with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-3-support-vector-machine-with-math-47d6193c82be

  8. Chapter 3.1 : SVM from Scratch in Python. : https://medium.com/deep-math-machine-learning-ai/chapter-3-1-svm-from-scratch-in-python-86f93f853dc

  9. Chapter 4: Decision Trees Algorithms : https://medium.com/deep-math-machine-learning-ai/chapter-4-decision-trees-algorithms-b93975f7a1f1

  10. Chapter 5 : K-nearest neighbors algorithm with code from scratch. : https://medium.com/deep-math-machine-learning-ai/chapter-5-k-nearest-neighbors-algorithm-with-code-from-scratch-7f93f653c860

  11. Chapter 7 : Artificial neural networks with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-7-artificial-neural-networks-with-math-bb711169481b

  12. Chapter 7.1 : Neural network from scratch in python : https://medium.com/deep-math-machine-learning-ai/chapter-7-1-neural-network-from-scratch-in-python-b880b0ff5f7b

  13. Chapter 8 .0: Convolutional neural networks for deep learning. : https://medium.com/deep-math-machine-learning-ai/chapter-8-0-convolutional-neural-networks-for-deep-learning-364971e34ab2

  14. Chapter 8 .1: Code for Convolutional neural networks(Tensorflow and Keras-Theano). : https://medium.com/deep-math-machine-learning-ai/chapter-8-1-code-for-convolutional-neural-networks-tensorflow-and-keras-theano-33bef285dd93

  15. Chapter 9 : Natural Language Processing. : https://medium.com/deep-math-machine-learning-ai/chapter-9-natural-language-processing-14bbeb8edc79

  16. Chapter 9.1 : NLP - Word vectors. : https://medium.com/deep-math-machine-learning-ai/chapter-9-1-nlp-word-vectors-d51bff9628c1

  17. Chapter 9.2: NLP- Code for Word2Vec neural network(Tensorflow). : https://medium.com/deep-math-machine-learning-ai/chapter-9-2-nlp-code-for-word2vec-neural-network-tensorflow-544db99f5334

  18. Chapter 10: DeepNLP - Recurrent Neural Networks with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-10-deepnlp-recurrent-neural-networks-with-math-c4a6846a50a2

  19. Chapter 10.1: DeepNLP — LSTM (Long Short Term Memory) Networks with Math. : https://medium.com/deep-math-machine-learning-ai/chapter-10-1-deepnlp-lstm-long-short-term-memory-networks-with-math-21477f8e4235

  20. Chapter 11: ChatBots to Question & Answer systems. : https://medium.com/deep-math-machine-learning-ai/chapter-11-chatbots-to-question-answer-systems-e06c648ac22a

  21. Ch 12:Reinforcement learning Complete Guide #towardsAGI : https://medium.com/deep-math-machine-learning-ai/ch-12-reinforcement-learning-complete-guide-towardsagi-ceea325c5d53

  22. Ch 12.1:Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) : https://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4

  23. Ch:13: Deep Reinforcement learning — Deep Q-learning and Policy Gradients ( towards AGI ). : https://medium.com/deep-math-machine-learning-ai/ch-13-deep-reinforcement-learning-deep-q-learning-and-policy-gradients-towards-agi-a2a0b611617e

  24. Ch:14 Generative Adversarial Networks (GAN’s) with Math : https://medium.com/deep-math-machine-learning-ai/ch-14-general-adversarial-networks-gans-with-math-1318faf46b43

  25. Ch:14.1 Types of GAN’s with Math. : https://medium.com/deep-math-machine-learning-ai/ch-14-1-types-of-gans-with-math-5b0dbc1a491d

Data science Article that explain Andrew ng course

  1. “Data Science Simplified Part 1: Principles and Process” by Pradeep Menon : https://link.medium.com/LSGehge2oS

  2. “Data Science Simplified Part 2: Key Concepts of Statistical Learning” by Pradeep Menon : https://link.medium.com/nSgsATh2oS

  3. “Data Science Simplified Part 3: Hypothesis Testing” by Pradeep Menon : https://link.medium.com/NsZcT2i2oS

  4. “Data Science Simplified Part 4: Simple Linear Regression Models” by Pradeep Menon : https://link.medium.com/tiTuAdk2oS

  5. “Data Science Simplified Part 5: Multivariate Regression Models” by Pradeep Menon : https://link.medium.com/V7wCbEl2oS

  6. “Data Science Simplified Part 6: Model Selection Methods” by Pradeep Menon : https://link.medium.com/R4npbRm2oS

  7. “Data Science Simplified Part 7: Log-Log Regression Models” by Pradeep Menon : https://link.medium.com/6RzgmXn2oS

  8. “Data Science Simplified Part 8: Qualitative Variables in Regression Models” by Pradeep Menon : https://link.medium.com/Il7nt5p2oS

  9. “Data Science Simplified Part 9: Interactions and Limitations of Regression Models” by Pradeep Menon : https://link.medium.com/rNQxcmr2oS

  10. “Data Science Simplified Part 10: An Introduction to Classification Models” by Pradeep Menon : https://link.medium.com/ELrZtws2oS

  11. “Data Science Simplified Part 11: Logistic Regression” by Pradeep Menon : https://link.medium.com/k7GKaVt2oS

Statistics and Probability article series

  1. “Probability & Statistics for Data Science (Series)” by Ankit Rathi : https://link.medium.com/8h1yER61oS
  2. “Probability for Data Science” by Ankit Rathi : https://link.medium.com/AiusCXX1oS
  3. “Descriptive Statistics for Data Science” by Ankit Rathi : https://link.medium.com/3J1mRN01oS
  4. “Inferential Statistics for Data Science” by Ankit Rathi : https://link.medium.com/FM7Sk411oS
  5. “Bayesian Statistics for Data Science” by Ankit Rathi : https://link.medium.com/1eoZ5d31oS
  6. “Statistical Learning for Data Science” by Ankit Rathi : https://link.medium.com/jklLLw41oS

Machine Learning Algorithms

  1. “Chapter 1 : Supervised Learning and Naive Bayes Classification — Part 1 (Theory)” by Savan Patel : https://link.medium.com/D2R8tTk1oS

  2. “Chapter 1 : Supervised Learning and Naive Bayes Classification — Part 2 (Coding)” by Savan Patel : https://link.medium.com/xkYZ6Bn1oS

  3. “Chapter 2 : SVM (Support Vector Machine) — Coding” by Savan Patel : https://link.medium.com/ck3zWYN1oS

  4. “Chapter 2 : SVM (Support Vector Machine) — Theory” by Savan Patel : https://link.medium.com/wxBBkup1oS

  5. “Chapter 3 : Decision Tree Classifier — Theory” by Savan Patel : https://link.medium.com/nCPgMQq1oS

  6. “Chapter 3 : Decision Tree Classifier — Coding” by Savan Patel : https://link.medium.com/piuCzcs1oS

  7. “Chapter 4: K Nearest Neighbors Classifier” by Savan Patel : https://link.medium.com/GJ7JxDt1oS

  8. “Chapter 5: Random Forest Classifier” by Savan Patel : https://link.medium.com/gg8hNav1oS

  9. “Chapter 6: Adaboost Classifier” by Savan Patel : https://link.medium.com/H1A63nw1oS