• Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

• Carefully curated resource links for data science in one place

• 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):

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

• Detailed Numpy operations
• Detailed Pandas operations
• Numpy and Pandas quick basics
• Matplotlib and Seaborn quick basics
• Advanced Pandas operations
• How to read various data sources
• PDF reading and table processing demo
• How fast are Numpy operations compared to pure Python code? (Read my article on Medium related to this topic)
• Fast reading from Numpy using .npy file format (Read my article on Medium on this topic)

• Simple linear regression with t-statistic generation

• Multiple ways to perform linear regression in Python and their speed comparison (check the article I wrote on freeCodeCamp)

• Multi-variate regression with regularization

• Polynomial regression using scikit-learn pipeline feature (check the article I wrote on Towards Data Science)

• Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)

• Detailed visual analytics and goodness-of-fit diagnostic tests for a linear regression problem

• Robust linear regression using HuberRegressor from Scikit-learn

• Logistic regression/classification (Here is the Notebook)

• k-nearest neighbor classification (Here is the Notebook)

• Decision trees and Random Forest Classification (Here is the Notebook)

• Support vector machine classification (Here is the Notebook) (check the article I wrote in Towards Data Science on SVM and sorting algorithm)

• Naive Bayes classification (Here is the Notebook)

• 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)

• Principal component analysis

• Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation task
• Step-by-step building of 1-hidden-layer and 2-hidden-layer dense network using basic TensorFlow methods

• How to use Sympy package to generate random datasets using symbolic mathematical expressions.

• Here is my article on Medium on this topic: Random regression and classification problem generation with symbolic expression

• Notebooks here

• Serving a linear regression model through a simple HTTP server interface. User needs to request predictions by executing a Python script. Uses Flask and Gunicorn.

• Serving a recurrent neural network (RNN) through a HTTP webpage, complete with a web form, where users can input parameters and click a button to generate text based on the pre-trained RNN model. Uses Flask, Jinja, Keras/TensorFlow, WTForms.

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.

• Object-oriented programming for data scientists: Build your ML estimator
• How a simple mix of object-oriented programming can sharpen your deep learning prototype

Check the files and detailed instructions in the Pytest directory to understand how one should write unit testing code/module for machine learning models

Profiling data science code and ML models for memory footprint and computing time is a critical but often overlooed area. Here are a couple of Notebooks showing the ideas,

• Memory profling using Scalene
• Time-profiling data science code