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The first section is an intentionally brief, functional, data science centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible.

The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, commandline tools, recommendation engines, Sentiment Analysis and Cloud Computing.

These notebooks and tutorials were produced byPragmatic AI Labs. You can continue learning about these topics by:

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• Watching 8+ Hour Video Series on Safari:Essential Machine Learning and AI with Python and Jupyter Notebook
• Reading online with Safari:Pragmatic AI: An Introduction to Cloud-Based Machine Learning, First Edition

• Introductory Concepts in Python, IPython and Jupyter
• Functions

• Writing And Using Libraries In Python
• Understanding Python Classes
• Control Structures
• Understanding Sorting
• Python Regular Expressions

• Working with Files
• Serialization Techniques
• Use Pandas DataFrames
• Concurrency in Python
• Walking through Social Power NBA EDA and ML Project

• Introducing AWS Web Services: Creating accounts, Creating Users and Using Amazon S3
• Using Boto
• Starting development with AWS Python Lambda development with Chalice
• Using of AWS DynamoDB
• Using of Step functions with AWS
• Using of AWS Batch for ML Jobs
• Using AWS Sagemaker for Deep Learning Jobs
• Using AWS Comprehend for NLP
• Using AWS Image Recognition API

Local, non-hosted versions of these notebooks are here:https://github.com/noahgift/functional_intro_to_python/tree/master/colab-notebooks

• Screencast: How to setup a Python Project in Github, Test it with Pytest, use Pylint and Build it With CircleCI
• Screencast: How to launch AWS Spot Instances and Create Custom AMIs
• Screencast: How to use AWS S3 including from Pandas and Boto inside Jupyter

• Lesson1: Introductory Concepts
• Lesson2: Functions
• Lesson3: Control Structures
• Lesson4: Intermediate Topics: Classes, Modules, Libraries
• Lesson5: IO in Python

• Managed Machine Learning Systems and Internet of Things

• How Create a Python Project Github Repository
• How to Write "Clean" Code in Python (2010) Using Pylint
• How to Test Jupyter Notebooks with Pytest
• How to build and test a Python Project with CircleCI
• How to get test Coverage with Pytest
• How to use Pylint to Fail on Error and Warnings only

• IBM Developerworks: Writing Multi-Threaded Programs in Python (2008)
• IBM Developerworks: Using Multi-processing Module in Python (2009)
• Writing Async Network IO Calls to AWS API
• Worker Farm with RabbitMQ and Tornado

• AWS Sentiment Analysis Notebook

• Hand coded tanimoto and Pandas similarity engine

• NLP on Azure

• AWS + Boto: Python and AWS Jupyter Notebook
• AWS + Boto: Launching Spot Instances From Python
• AWS + Boto: Calling Spot Instance API to Create CLI Machine Learning Tool
• AWS + Boto: Spot Price Jupyter Notebook Exploration

• Google Sheets to Pandas DataFrame
• BigQuery to Pandas to Seaborn
• NLP on Google Cloud

• DEVML: Datascience around Github
• Social Power NBA: Datascience around the NBA and Social Media
• Spot Price ML(KMeans Unsupervized Machine Learning Recommender): Datascience around AWS Spot Prices

• Visualizing Spot Prices in Seaborn
• Correlation Heatmap in Seaborn for NBA

• Exploring 30 years of Real Estate Prices with Zillow Data and Plotly

• Python Commandline tool Rosetta: Comparing R, Bash, Go, Node, Python and Ruby
• Pyli: Deduplication Commandline Tool That Walks A Filesystem
• Developersworks Article (2008): Creating Commandline Tools Python

• Real world Data Engineering API

• Interfacing with Hugo and Algolia from Python

• Article on how to deploy commandline tools and libraries to PyPi

• Programatically Scraping Wikipedia API in Python: Pageviews and Articles

• Using Python Twitter API

• Serverless Web Scraping with Lambda and Step Functions

• "Sensible" Logging Library in Python: Deployed to PyPI

• IBM Developerworks Series on Pyomo (Linear Optimization in Python)

• Traveling Salesman (NP Hard Simulation Solution with Random, Greedy Start)

• Greedy Coin Example in Python

• Linear Regression in Jupyter Notebook

The text content of notebooks is released under theCC-BY-NC-ND license