Machine-Learning-2018-present-

These are all the machine learning codes that I have used for learning and teaching.

The files and folders are bifurcated in the following way -

0. Basics

• This folder contains some basic jupyter notebooks for Data Science libraries like NumPy, Matplotlib and Pandas.

1. Supervised Learning

• Regression
  • Contains multiple implementations of Linear and Polynomial Regression using different methods like the Normal Equation, Gradient Descent and also an easy implementation with the Scikit-Learn library.
• Classification
  • Contains multiple implementations (from scratch and using scikit-learn) of Classification Algorithms like K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Decision Trees and Naive Bayes.
  • Also consists of Neural Network Implementations in Tensorflow like Artificial Neural Network (ANN), Convolutional Neural Networks for Image Classification, Recurrent Neural Networks (LSTMs) for Sentence Classification & Sequence Tagging.

2. Unsupervised Learning

• Association Algorithms
  • Consists of Association Rule Mining Algorithms to find patterns in raw data like Apriori Algorithm and FP-Growth.
• Clustering Algorithms
  • Contains implementations (from scratch and using scikit-learn) for K-Means and Meanshift clustering.
• Dimensionality Reduction
  • Contains implementation of PCA (Principal Component Analysis) Dimensionality Reduction algorithm.

3. Reinforcement Learning

• Contains multiple implementations of Reinforcement Learning Algorithms like Multiarmed Bandits, Q-Learning, Deep Q Networks with Experience Replay

4. Anomaly Detection

• Contains implementation of an Anomaly Detection algorithm using Gaussian Estimation.