Essential codes for jump-starting machine learning/data science with Python

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

• Simple linear regression with t-statistic generation
• Multiple ways to do linear regression in Python and their speed comparison (check the article I wrote on freeCodeCamp)
• Multi-variate regression with regularization
• Polynomial regression with how to use 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)

• Logistic regression/classification
• k-nearest neighbor classification
• Decision trees and Random Forest Classification
• Support vector machine classification
• Naive Bayes classification

• 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)
• Hierarchical clustering with Dendograms showing how to choose optimal number of clusters
• DBSCAN (showing how it can generically detect areas of high density irrespective of cluster shapes, which the k-means fails to do)

• 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

• Demo on how to integrate basic interactive controls (slider bars, drop-down menus, check-boxes etc.) in a Jupyter notebook and use them for interactive machine learning task

The https://github.com/machine-learning-helpers/docker-python-jupyter project builds a Docker image so that the (your) Jupyter notebooks can be run out-of-the-box on almost any platform in a few minutes.

It gives something like:

• Initialization of the Git repository for the Jupyter notebooks:

$ mkdir -p ~/dev/ml
$ cd ~/dev/ml
$ git clone https://github.com/tirthajyoti/PythonMachineLearning.git

Initialization of the Docker image to run those Jupyter notebooks:

$ docker pull artificialintelligence/python-jupyter

Usgae

$ cd ~/dev/ml/PythonMachineLearning
$ docker run -d -p 9000:8888 -v ${PWD}:/notebook -v ${PWD}:/data artificialintelligence/python-jupyter

And then you can open http://localhost:9000 in your browser.

Any modification to the notebooks may be committed to the Git repository (if you are registered as a contributor), and/or submitted as a pull request. Shutdown

$ docker ps
CONTAINER ID        IMAGE                                   COMMAND                  CREATED             STATUS              PORTS                    NAMES
431b12a93ccf        artificialintelligence/python-jupyter   "/bin/sh -c 'jupyt..."   4 minutes ago       Up 4 minutes        0.0.0.0:9000->8888/tcp   friendly_euclid
$ docker kill 431b12a93ccf 

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