https://github.com/amir22010/machine-learning-with-python

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

https://github.com/amir22010/machine-learning-with-python

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

https://github.com/Amir22010/Machine-Learning-with-Python/blob/master/

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# Python Machine Learning Notebooks (Tutorial style)

### Dr. Tirthajyoti Sarkar, Fremont, CA ([Please feel free to add me on LinkedIn here](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7))
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### Requirements
* **Python 3.5+**
* **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`)**
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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.

* [Basic Numpy operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Numpy%20arrays.ipynb)
* [Basic Pandas operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Pandas%20DataFrame.ipynb)
* [Basics of visualization with Matplotlib and descriptive stats](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Matplotlib%20and%20Descriptive%20Statistics.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 (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)

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### Classification
* Logistic regression/classification


* _k_-nearest neighbor classification
* Decision trees and Random Forest Classification
* Support vector machine classification (**[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

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### Clustering


* _K_-means clustering
* Affinity propagation (showing its time complexity and the effect of damping factor)
* Mean-shift technique (showing its time complexity and the effect of noise on cluster discovery)
* DBSCAN (showing how it can generically detect areas of high density irrespective of cluster shapes, which the k-means fails to do)
* Hierarchical clustering with Dendograms showing how to choose optimal number of clusters


---

### Dimensionality reduction
* Principal component analysis



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### 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)
---

### 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)

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