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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.
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These notebooks and tutorials were produced by Pragmatic AI Labs. You can continue learning about these topics by:
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His most recent books are:
- Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
- Python for DevOps (O'Reilly, 2020).
- Cloud Computing for Data Analysis, 2020
- Testing in Python, 2020
His most recent video courses are:
- Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
- AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
- Python for Data Science Complete Video Course Video Training (Pearson, 2019)
- AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
- Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
- Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
- Data Engineering with Python and AWS Lambda (Pearson, 2019)
His most recent online courses are:
- Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)
- Command Line Automation in Python (DataCamp, 2019)
- AWS Certified Cloud Practitioner 2020-Real World & Pragmatic.
Safari Online Training: Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook
- 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
- Watching learning path Essential Machine Learning and Pragmatic AI
- Watching video AWS Certified Machine Learning-Speciality
- Exploring interactive Jupyter book on "Python For Data Science"
- Introductory Concepts in Python, IPython and Jupyter
- Functions
1.3: Understanding Libraries, Classes, Control Structures, Control Structures and Regular Expressions
- 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
- Data Science Build Project
- 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
- 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
- Increase reliability in data science and machine learning projects with CircleCI
- 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
- Nuclear Powered Command-Line Tools: GPU/CUDA, JIT, Multi-threaded JIT
- 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
- 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
- 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
- Nuclear Powered Commandline tools
-
IBM Developerworks Series on Pyomo (Linear Optimization in Python)
-
Traveling Salesman (NP Hard Simulation Solution with Random, Greedy Start)
The text content of notebooks is released under the CC-BY-NC-ND license