Reference

Implementing algorithms from scratch so I can learn more about machine learning, statistics and computer science.

I also have some reference materials I've contributed to my company's GitHub repo.

Notebooks

Data Analysis

• Deep learning

  • DataCamp Deep Learning [1]
  • DataCamp Introduction to TensorFlow in Python [1]
  • DataCamp Intro to Deep Learning with Keras [1]
  • DataCamp Image Processing with Keras in Python [1]
  • DataCamp Advanced Deep Learning with Keras [1]
  • DataCamp Pytorch [1]
  • PythonProgramming Pytorch [1]
  • PythonProgramming Keras Tensorflow [1]
  • Intro to Keras with diabetes dataset and saving models [1, 2, 3, 4, 5]
  • Intro to Keras with MNIST [1, 2]
  • Using Keras to solve MNIST [1, 2]
  • Regression Tutorial with the Keras [1]
  • [Keras Multi-Class Classification on Iris Dataset]deep_learning/keras_iris_tutorial.ipynb) [1]
  • Binary classification of sonar data using Keras [1]

• Reinforcement Learning

  • Q-Learning Reinforcement Learning with PythonProgramming [1]

• Spatial

  • Visualising Geospatial data in Python DataCamp [1]
  • Spatial Analysis with sf and raster in R [1]

• Networks

  • Networkx [1, 2, 3]
  • Networks DataCamp 1 [1]
  • Networks DataCamp 2 [1]

• Machine Learning Recipes [1]

  • Decision Trees and visualising decisions
  • Implementing KNN and Euclidean distance from scratch

• Programmers Guide to Data Mining [1]

  • Collaborative filtering: pearson correlation, cosine similarity, KNN
  • Implicit ratings and item based filtering
  • Classification based on item attributes
  • Evaluating Algorithms
  • Naïve Bayes
  • Unstructured Text
  • Clustering

• Practical Machine Learning [1]

  • Sklearn regression, SVM & pickling with share market data
  • Implementing regression from scratch
  • Unsupervised Machine Learning: k-means & mean-shift [1]

• Supervised

  • Supervised Learning in Python DataCamp [1]
  • School Budgets Classification DataCamp [1]

• Unsupervised

  • Visualising High Dimensional Datasets with PCA and T-SNE [1]
  • Unsupervised Learning in Python DataCamp [1]

• Ensemble methods

  • House Price Estimation [1]
  • Tour of ensemble methods [1]
  • XGBoost Machine Learning Mastery [1]
  • Extreme Gradient Boosting with XGBoost DataCamp

• NLP

  • Machine Learning with Text: Sklearn & bag of words [1]
  • NLTK with Python 3 for Natural Language Processing [1, 2]

• Statistics

  • Bayes Made Simple [1, 2, 3, 4]

• Time Series

  • Introduction to Time Series Analysis in Python DataCamp [1]

• Big Data

  • Dask DataCamp [1]
  • PySpark DataCamp [1]

• Linear Optimization

  • Supply Chain Analytics in Python DataCamp [1]
  • Linear Optimization of Ted Talks [1]

• R

  • Introduction to the Tidyverse DataCamp [1]
  • Joe's tutorial notes: covers tidyverse, figures, dates
  • Plexos Post Processing using multiprocessing to batch process CSVs

• SQL

  • SQL Reference [1]
  • SQL in the Jupyter Notebook

• Visualisation

  • Bokeh DataCamp [1]
  • Building Web Applications with Shiny in R DataCamp [1]
  • Interactive Maps with leaflet in R DataCamp [1]

Programming

• Web Scraping with Beautiful Soup [1]
• Python Multiprocessing [1, 2]
• Python Decorators [1]
• MyPy Python Type Checker [1]
• Python Base64 Encode/Decode & PIL
• Python Args & Kwargs [1]
• Python Logging
• SQL in the Jupyter Notebook
• Cython: Compiling Python