Data Science Resources - Let's grow together

Intro To DataScience

Microsoft-Beginners DS
Data Science Resource
Cheet Sheets and BOOKS <3

Kaggle's 30 Days of ML - Abhishek Thakur ⭐
Applied ML Framework - Abhishek Thakur ⭐⭐
Machine Learning — Andrew Ng ⭐
Statistics Fundamentals - Josh Starmer ⭐⭐
Statistics in Machine Learning - Krish Naik ⭐

Python

• Python for PRO

Maths for ML

• Mathematics for ML - Seminar_notes - IMPERIAL COLLEGE LONDON
• Mathematics for ML - LECTURE NOTES - IMPERIAL COLLEGE LONDON
• Mathematics for ML - Cambridge University
• Mathematics for Machine Learning - GOD ⭐⭐

Machine Learning With Python

• ML with Python Cookbook-Chris Albon ⭐⭐

These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.

• Regression
• Linear Regression
• Ordinary Least Squares
• Logistic Regression
• Stepwise Regression
• Multivariate Adaptive Regression Splines
• Locally Estimated Scatterplot Smoothing
• Classification
  • k-nearest neighbor
  • Support Vector Machines
  • Decision Trees
  • ID3 algorithm
  • C4.5 algorithm
• Ensemble Learning
• Boosting
• Bagging
• Random Forest
• AdaBoost

• Clustering
  • Hierchical clustering
  • k-means
  • Fuzzy clustering
  • Mixture models
• Dimension Reduction
  • Principal Component Analysis (PCA)
  • t-SNE
• Neural Networks
• Self-organizing map
• Adaptive resonance theory
• Hidden Markov Models (HMM)

• S3VM
• Clustering
• Generative models
• Low-density separation
• Laplacian regularization
• Heuristic approaches

• Q Learning
• SARSA (State-Action-Reward-State-Action) algorithm
• Temporal difference learning

• C4.5
• k-Means
• SVM
• Apriori
• EM
• PageRank
• AdaBoost
• kNN
• Naive Bayes
• CART

• Multilayer Perceptron
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Boltzmann Machines
• Autoencoder
• Generative Adversarial Network (GAN)
• Self-Organized Maps