ipython-notebook

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Instructional notebooks on music information retrieval.

strip output from Jupyter and IPython notebooks

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

CatBoost tutorials repository

Non-Intrusive Load Monitoring Toolkit (nilmtk)

Jupyter notebooks in the terminal

Scipy Cookbook

A tiny 1000 line LLVM-based numeric specializer for scientific Python code.

A py.test plugin to validate Jupyter notebooks

IPython Notebooks to learn Python

Jupyter for Visual Studio Code

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

Code and Examples for Relevant Search

Real-time GCC-NMF Blind Speech Separation and Enhancement

Pytest in IPython notebooks.

WebRTC for Jupyter notebook/lab

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

Jupyter/IPython notebooks about evolutionary computation.

Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects. In this tutorial, you’ll learn the basics of object-oriented programming in Python.

You'll learn about Iterators, Generators, Closure, Decorators, Property, and RegEx in detail with examples.

Flow control is the order in which statements or blocks of code are executed at runtime based on a condition. Learn Conditional statements, Iterative statements, and Transfer statements

Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but like other concepts of Python, this concept here is also easy and short. Python treats files differently as text or binary and this is important.

Time is undoubtedly the most critical factor in every aspect of life. Therefore, it becomes very essential to record and track this component. In Python, date and time can be tracked through its built-in libraries. This article on Date and time in Python will help you understand how to find and modify the dates and time using the time and datetime modules.

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram, etc

SQLCell is a magic function for the Jupyter Notebook that executes raw, parallel, parameterized SQL queries with the ability to accept Python values as parameters and assign output data to Python variables while concurrently running Python code. And *much* more.

This repository contains all the data analytics projects that I've worked on in python.