References and Acknowledgements

Acknowledgements:

• Numpy Documentation

  ---

• Scikit Learn Documentation

  ---

• TensorFlow Documentation

  ---

• Open GPU Data Science (RAPIDS)

  ---

• MapReduce Tutorial – Fundamentals of MapReduce with MapReduce Example (Edureka)

  ---

• Sampling Techniques by Seema Singh (Towards Data Science)

  ---

• Amazon Whitepapers

  ---

• Data Mining: Concepts and Techniques by Jiawei Han Micheline Kamber Jian Pei

  ---

• Decision Tree Algorithm, Explained (kdnuggets)

  ---

• What is Hierarchical Clustering? (kdnuggets)

  ---

• Normal Distribution in Statistics (Statistics By Jim)

  ---

• Naïve Bayes Classifiers (GeeksforGeeks)

  ---

• Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

  ---

• Standard Deviation and Variance (Mathisfun)

  ---

• Normal Distribution (Mathisfun)

  ---

• Conditional Probability (Mathgoodies)

  ---

• Understanding Support Vector Machine (SVM) algorithm from examples (along with code) (Analytics Vidhya)

  ---

• 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R (Analytics Vidhya)

  ---

• Categorical Encoding (One Hot Encoding) in Feature Engineering (Analytics Vidhya)

  ---

• Pseudo-labeling a simple semi-supervised learning method (datawhatnow)

  ---

• Understanding LSTM Networks (Colah’s Blog)

  ---

• Association Rules Generation from Frequent Itemsets (mlxtend documentation)

  ---

• A STEP-BY-STEP EXPLANATION OF PRINCIPAL COMPONENT ANALYSIS (PCA) (Built In Blog)

  ---

• A SIMPLE INTRODUCTION TO COLLABORATIVE FILTERING (Built In Blog)

  ---

• Semi-supervised learning (Wikipedia)

  ---

• Machine Learning Mastery with Python

  ---

• Professor Andrew Ng

  ---

• Stacking in Machine Learning (Supunsetunga Blog)

  ---

• Saman Saman, The wonderful Reinforcement Learning

  ---

• Jason Brownlee, Data Preparation for Machine Learning, Machine Learning Mastery, and Statistical Methods for Machine Learning,

  ---

• Feras Fraige, Engineering Probability & Statistics (AGE 1150)

• Soledad Galli, Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build Machine Learning models,

  ---

• Kaggle Notebooks by Yassine Ghouzam, Ashwini swain, Robert Kwiatkowski, Fazil T, Vikum Sri Wijesinghe, Tuatini GODARD, Lavanya Shukla, Vikas Singh, Alexandru Papiu

  ---