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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- Cloud Computing (Specialization: 4 Courses)
- Publisher: Coursera + Duke
- Release Date: 4/1/2021
Building Cloud Computing Solutions at Scale Specialization Launch Your Career in Cloud Computing. Master strategies and tools to become proficient in developing data science and machine learning (MLOps) solutions in the Cloud
- Build websites involving serverless technology and virtual machines, using the best practices of DevOps
- Apply Machine Learning Engineering to build a Flask web application that serves out Machine Learning predictions
- Create Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: AWS, Azure or GCP
- Take the Specialization
- Cloud Computing Foundations
- Cloud Virtualization, Containers and APIs
- Cloud Data Engineering
- Cloud Machine Learning Engineering and MLOps
- Practical MLOps (O'Reilly 2021)
- 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
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- 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
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IBM Developerworks Series on Pyomo (Linear Optimization in Python)
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Traveling Salesman (NP Hard Simulation Solution with Random, Greedy Start)
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