August 25, 2019

661 words 4 mins read



Practice and tutorial-style notebooks covering wide variety of machine learning techniques

repo name tirthajyoti/Machine-Learning-with-Python
repo link
language Jupyter Notebook
size (curr.) 95217 kB
stars (curr.) 1188
created 2017-07-17
license BSD 2-Clause “Simplified” License

License GitHub forks GitHub stars PRs Welcome

Python Machine Learning Jupyter Notebooks (ML website)

Dr. Tirthajyoti Sarkar, Fremont, California (Please feel free to connect on LinkedIn here)


  • Python 3.6+
  • NumPy (pip install numpy)
  • Pandas (pip install pandas)
  • Scikit-learn (pip install scikit-learn)
  • SciPy (pip install scipy)
  • Statsmodels (pip install statsmodels)
  • MatplotLib (pip install matplotlib)
  • Seaborn (pip install seaborn)
  • Sympy (pip install sympy)
  • Flask (pip install flask)
  • WTForms (pip install wtforms)
  • Tensorflow (pip install tensorflow>=1.15)
  • Keras (pip install keras)
  • pdpipe (pip install pdpipe)

You can start with this article that I wrote in Heartbeat magazine (on Medium platform):

“Some Essential Hacks and Tricks for Machine Learning with Python”

Essential tutorial-type notebooks on Pandas and Numpy

Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, Matplotlib etc.

Tutorial-type notebooks covering regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms




  • K-means clustering (Here is the Notebook)

  • Affinity propagation (showing its time complexity and the effect of damping factor) (Here is the Notebook)

  • Mean-shift technique (showing its time complexity and the effect of noise on cluster discovery) (Here is the Notebook)

  • DBSCAN (showing how it can generically detect areas of high density irrespective of cluster shapes, which the k-means fails to do) (Here is the Notebook)

  • Hierarchical clustering with Dendograms showing how to choose optimal number of clusters (Here is the Notebook)

Dimensionality reduction

  • Principal component analysis

Deep Learning/Neural Network

Random data generation using symbolic expressions

Simple deployment examples (serving ML models on web API)

Object-oriented programming with machine learning

Implementing some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

See my articles on Medium on this topic.

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