A Collection of Jupyter notebooks on "Deep Learning and Machine Learning with Python"

This repository aims to teach you the fundamentals of Machine Learning and Deep Learning in Python. It contains example codes and theoretical explanations on the various learning methods available in the field of Data Science.

Table of Contents

Theory

• Machine Learning
  • [1.1: Decision Tree]
  • [1.2: Linear Regression]
  • [1.3: Logistic Regression]
  • [1.4: K-Nearest Neigbour]
  • [1.5: Support Vector Machines]
  • [1.6: Ensemble Methods and Random Forest]
  • [Dimensional Reduction]
  • [Model Evaluation]

Tutorials

• Tensorflow
  • [2.1: Introduction to Neural Networks]
  • [2.2: Deep Learning]
  • [2.3: Distributed Tensorflow]
  • [2.4: Convolutional Neural Networks]
  • [2.5: Recurrent Neural Networks]
  • [2.6: autoencoders]
  • [2.7: Reinforcement Learning]

Examples

• Keras

  • [3.1: Deep Multi-Layer Perceptions]
  • [3.2: Convolutional Neural Network]
  • [3.3: Transfer Learning]

• Tensorflow

  • [4.1: Linear Regression]
  • [4.2: Logistic Regression]
  • [4.3: Nearest Neighbor]
  • [4.4: K-Means]
  • [4.5: Random Forest]
  • [4.6: Multi-layer Perception]
  • [4.7: Convolutional Neural Network]
  • [4.8: Recurrent Neural Network]
  • [4.9: Generative Adversarial Network]