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|License| |GitHub forks| |GitHub stars|
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Python Machine Learning Notebooks (Tutorial style)
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==================================================
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Authored and maintained by Dr. Tirthajyoti Sarkar, Fremont, CA. `Please
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feel free to add me on LinkedIn
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here <https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7>`__.
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--------------
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Requirements
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------------
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- Python 3.5
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- NumPy (``pip install numpy``)
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- Pandas (``pip install pandas``)
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- Scikit-learn (``pip install scikit-learn``)
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- SciPy (``pip install scipy``)
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- Statsmodels (``pip install statsmodels``)
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- MatplotLib (``pip install matplotlib``)
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- Seaborn (``pip install seaborn``)
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- Sympy (``pip install sympy``)
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--------------
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You can start with this article that I wrote in Heartbeat magazine (on
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Medium platform):
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`“Some Essential Hacks and Tricks for Machine Learning with
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Python” <https://heartbeat.fritz.ai/some-essential-hacks-and-tricks-for-machine-learning-with-python-5478bc6593f2>`__
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Essential tutorial-type notebooks on Pandas and Numpy
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-----------------------------------------------------
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Jupyter notebooks covering a wide range of functions and operations on
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the topics of NumPy, Pandans, Seaborn, matplotlib etc.
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- `Basic Numpy
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operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Numpy%20arrays.ipynb>`__
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- `Basic Pandas
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operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Pandas%20DataFrame.ipynb>`__
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- `Basics of visualization with Matplotlib and descriptive
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stats <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Matplotlib%20and%20Descriptive%20Statistics.ipynb>`__
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- `Advanced Pandas
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operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb>`__
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- `How to read various data
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sources <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/How%20to%20read%20various%20sources%20in%20a%20DataFrame.ipynb>`__
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- `PDF reading and table processing
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demo <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/PDF%20table%20reading%20and%20processing%20demo.ipynb>`__
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- `How fast are Numpy operations compared to pure Python
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code? <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/How%20fast%20are%20NumPy%20ops.ipynb>`__
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(Read my
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`article <https://towardsdatascience.com/why-you-should-forget-for-loop-for-data-science-code-and-embrace-vectorization-696632622d5f>`__
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on Medium related to this topic)
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- `Fast reading from Numpy using .npy file
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format <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Reading.ipynb>`__
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(Read my
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`article <https://towardsdatascience.com/why-you-should-start-using-npy-file-more-often-df2a13cc0161>`__
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on Medium on this topic)
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Regression
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----------
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- Simple linear regression with t-statistic generation (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Practice.ipynb>`__)
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- Multiple ways to perform linear regression in Python and their speed
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comparison (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb>`__).
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Also `check the article I wrote on
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freeCodeCamp <https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b>`__
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- Multi-variate regression with regularization (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Multi-variate%20LASSO%20regression%20with%20CV.ipynb>`__)
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- Polynomial regression using **scikit-learn pipeline feature** (`Here
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is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regularized%20polynomial%20regression%20with%20linear%20and%20random%20sampling.ipynb>`__).
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Also `check the article I wrote on Towards Data
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Science <https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49>`__.
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- Decision trees and Random Forest regression (showing how the Random
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Forest works as a robust/regularized meta-estimator rejecting
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overfitting) (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Random_Forest_Regression.ipynb>`__).
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- Detailed visual analytics and goodness-of-fit diagnostic tests for a
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linear regression problem (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb>`__).
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--------------
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Classification
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--------------
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- Logistic regression/classification (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb>`__).
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- *k*-nearest neighbor classification (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb>`__).
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- Decision trees and Random Forest Classification (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb>`__).
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- Support vector machine classification (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb>`__).
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Also `check the article I wrote in Towards Data Science on SVM and
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sorting
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algorithm <https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b>`__.
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- Naive Bayes classification (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb>`__).
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--------------
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Clustering
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----------
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- *K*-means clustering (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb>`__).
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- Affinity propagation (showing its time complexity and the effect of
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damping factor) (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb>`__).
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- Mean-shift technique (showing its time complexity and the effect of
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noise on cluster discovery) (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb>`__).
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- DBSCAN (showing how it can generically detect areas of high density
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irrespective of cluster shapes, which the k-means fails to do) (`Here
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is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb>`__).
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- Hierarchical clustering with Dendograms showing how to choose optimal
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number of clusters (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb>`__).
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--------------
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Dimensionality reduction
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------------------------
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- Principal component analysis (`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Principal%20Component%20Analysis.ipynb>`__)
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--------------
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Random data generation using symbolic expressions
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-------------------------------------------------
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- Simple script to generate random polynomial expression/function
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(`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Random_function_generator.ipynb>`__).
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- How to use `Sympy package <https://www.sympy.org/en/index.html>`__ to
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generate random datasets using symbolic mathematical expressions
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(`Here is the
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Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Symbolic%20regression%20classification%20generator.ipynb>`__).
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Also, `here is the Python
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script <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Symbolic_regression_classification_generator.py>`__
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if anybody wants to use it directly in their project.
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- Here is my article on Medium on this topic: `Random regression and
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classification problem generation with symbolic
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expression <https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d>`__
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--------------
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Simple deployment examples (serving ML models on web API)
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---------------------------------------------------------
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- `Serving a linear regression model through a simple HTTP server
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interface <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression>`__.
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User needs to request predictions by executing a Python script. Uses
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``Flask`` and ``Gunicorn``.
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- `Serving a recurrent neural network (RNN) through a HTTP
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webpage <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app>`__,
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complete with a web form, where users can input parameters and click
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a button to generate text based on the pre-trained RNN model. Uses
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``Flask``, ``Jinja``, ``Keras``/``TensorFlow``, ``WTForms``.
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--------------
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Object-oriented programming with machine learning
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-------------------------------------------------
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Implementing some of the core OOP principles in a machine learning
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context by `building your own Scikit-learn-like estimator, and making it
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better <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb>`__.
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`Here is the complete Python script with the linear regression
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class <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.py>`__,
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which can do fitting, prediction, cpmputation of regression metrics,
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plot outliers, plot diagnostics (linearity, constant variance, etc.),
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compute variance inflation factors.
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See my articles on Medium on this topic.
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- `Object-oriented programming for data scientists: Build your ML
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estimator <https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64>`__
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- `How a simple mix of object-oriented programming can sharpen your
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deep learning
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prototype <https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd>`__
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.. |License| image:: https://img.shields.io/badge/License-BSD%202--Clause-orange.svg
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:target: https://opensource.org/licenses/BSD-2-Clause
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.. |GitHub forks| image:: https://img.shields.io/github/forks/tirthajyoti/Machine-Learning-with-Python.svg
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:target: https://github.com/tirthajyoti/Machine-Learning-with-Python/network
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.. |GitHub stars| image:: https://img.shields.io/github/stars/tirthajyoti/Machine-Learning-with-Python.svg
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:target: https://github.com/tirthajyoti/Machine-Learning-with-Python/stargazers

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