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https://github.com/tirthajyoti/machine-learning-with-python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
https://github.com/tirthajyoti/machine-learning-with-python
artificial-intelligence classification clustering data-science decision-trees deep-learning dimensionality-reduction flask k-nearest-neighbours machine-learning matplotlib naive-bayes neural-network numpy pandas pytest random-forest regression scikit-learn statistics
Last synced: 5 days ago
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Practice and tutorial-style notebooks covering wide variety of machine learning techniques
- Host: GitHub
- URL: https://github.com/tirthajyoti/machine-learning-with-python
- Owner: tirthajyoti
- License: bsd-2-clause
- Created: 2017-07-17T03:06:13.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-05-22T22:28:39.000Z (over 1 year ago)
- Last Synced: 2025-01-17T00:08:53.593Z (5 days ago)
- Topics: artificial-intelligence, classification, clustering, data-science, decision-trees, deep-learning, dimensionality-reduction, flask, k-nearest-neighbours, machine-learning, matplotlib, naive-bayes, neural-network, numpy, pandas, pytest, random-forest, regression, scikit-learn, statistics
- Language: Jupyter Notebook
- Homepage: https://machine-learning-with-python.readthedocs.io/en/latest/
- Size: 96.7 MB
- Stars: 3,144
- Watchers: 157
- Forks: 1,808
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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# Python Machine Learning Jupyter Notebooks ([ML website](https://machine-learning-with-python.readthedocs.io/en/latest/))
### Dr. Tirthajyoti Sarkar, Fremont, California ([Please feel free to connect on LinkedIn here](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7))
---
## Also check out these super-useful Repos that I curated
- ### [Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)
- ### [Carefully curated resource links for data science in one place](https://github.com/tirthajyoti/Data-science-best-resources)
## Requirements
* **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"](https://heartbeat.fritz.ai/some-essential-hacks-and-tricks-for-machine-learning-with-python-5478bc6593f2)
## 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.
* [Detailed Numpy operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_operations.ipynb)
* [Detailed Pandas operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Pandas_Operations.ipynb)
* [Numpy and Pandas quick basics](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Pandas_Quick.ipynb)
* [Matplotlib and Seaborn quick basics](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Matplotlib_Seaborn_basics.ipynb)
* [Advanced Pandas operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb)
* [How to read various data 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)
* [PDF reading and table processing 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)
* [How fast are Numpy operations compared to pure Python code?](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/How%20fast%20are%20NumPy%20ops.ipynb) (Read my [article](https://towardsdatascience.com/why-you-should-forget-for-loop-for-data-science-code-and-embrace-vectorization-696632622d5f) on Medium related to this topic)
* [Fast reading from Numpy using .npy file format](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Reading.ipynb) (Read my [article](https://towardsdatascience.com/why-you-should-start-using-npy-file-more-often-df2a13cc0161) on Medium on this topic)
## Tutorial-type notebooks covering regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms
### Regression
* Simple linear regression with t-statistic generation
* [Multiple ways to perform linear regression in Python and their speed comparison](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb) ([check the article I wrote on freeCodeCamp](https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b))
* [Multi-variate regression with regularization](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Multi-variate%20LASSO%20regression%20with%20CV.ipynb)
* Polynomial regression using ***scikit-learn pipeline feature*** ([check the article I wrote on *Towards Data Science*](https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49))
* [Decision trees and Random Forest regression](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Random_Forest_Regression.ipynb) (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](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb)
* [Robust linear regression using `HuberRegressor` from Scikit-learn](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Robust%20Linear%20Regression.ipynb)
-----
### Classification
* Logistic regression/classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb))
* _k_-nearest neighbor classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb))
* Decision trees and Random Forest Classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb))
* Support vector machine classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb)) (**[check the article I wrote in Towards Data Science on SVM and sorting algorithm](https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b))**
* Naive Bayes classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb))
---
### Clustering
* _K_-means clustering ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb))
* Affinity propagation (showing its time complexity and the effect of damping factor) ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb))
* Mean-shift technique (showing its time complexity and the effect of noise on cluster discovery) ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb))
* 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](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb))
* Hierarchical clustering with Dendograms showing how to choose optimal number of clusters ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb))
---
### Dimensionality reduction
* Principal component analysis
---
### Deep Learning/Neural Network
* [Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation task](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Polynomial%20regression%20-%20linear%20and%20neural%20network.ipynb)
* Step-by-step building of 1-hidden-layer and 2-hidden-layer dense network using basic TensorFlow methods
---
### Random data generation using symbolic expressions
* How to use [Sympy package](https://www.sympy.org/en/index.html) 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](https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d)
---
### Synthetic data generation techniques
* [Notebooks here](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Synthetic_data_generation)
### Simple deployment examples (serving ML models on web API)
* [Serving a linear regression model through a simple HTTP server interface](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression). User needs to request predictions by executing a Python script. Uses `Flask` and `Gunicorn`.
* [Serving a recurrent neural network (RNN) through a HTTP webpage](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app), 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`.
---
### 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](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb).
See my articles on Medium on this topic.
* [Object-oriented programming for data scientists: Build your ML estimator](https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64)
* [How a simple mix of object-oriented programming can sharpen your deep learning prototype](https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd)
---
### Unit testing ML code with Pytest
Check the files and detailed instructions in the [Pytest](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Pytest) directory to understand how one should write unit testing code/module for machine learning models
---
### Memory and timing profiling
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](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Memory-profiling/Scalene)
* [Time-profiling data science code](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Time-profiling/cProfile.ipynb)