Docs

README.md

@@ -1,39 +1,54 @@
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+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tirthajyoti/Machine-Learning-with-Python/pulls)
 
-# Python Machine Learning Notebooks (Tutorial style)
+# 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))
 
-### Dr. Tirthajyoti Sarkar, Fremont, CA ([Please feel free to add me on LinkedIn here](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7))
 ---
 
-### 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`)**
+## 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)
 <img src="https://cookieegroup.com/wp-content/uploads/2018/10/2-1.png" width="450" height="300"/>
 
 ## 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)
+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
 
@@ -47,34 +62,44 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/L1_and_L2_balls.svg/300px-L1_and_L2_balls.svg.png"/>
 
 * 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)
+
+* [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
+* Logistic regression/classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb))
 <img src="https://qph.fs.quoracdn.net/main-qimg-914b29e777e78b44b67246b66a4d6d71"/>
 
-* _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))**
+* _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))**
 
 <img src="https://docs.opencv.org/2.4/_images/optimal-hyperplane.png"/>
 
-* Naive Bayes classification
+* Naive Bayes classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb))
 
 ---
 
 ### Clustering
 <img src="https://i.ytimg.com/vi/IJt62uaZR-M/maxresdefault.jpg" width="450" height="300"/>
 
-* _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
+* _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))
+
 <img src="https://www.researchgate.net/profile/Carsten_Walther/publication/273456906/figure/fig3/AS:294866065084419@1447312956501/Example-of-hierarchical-clustering-clusters-are-consecutively-merged-with-the-most.png" width="700" height="400"/>
 
 ---
@@ -96,8 +121,12 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 * 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`.
 
@@ -113,3 +142,6 @@ 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