Data Science Resources - Let's grow together
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
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
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