Functional, Data Science Intro To Python
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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PYTHON in ONE HOUR
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Pragmatic AI Labs
These notebooks and tutorials were produced by Pragmatic AI Labs. You can continue learning about these topics by:
- 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
What You Will Learn
- 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
Courses in Specialization
His most recent books are:
His most recent video courses are:
His most recent online courses are:
Safari Online Training: Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook
Recommended Preparation Material:
1.1-1.2: Introductory Concepts in Python, IPython and Jupyter
- 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
2.1: IO Operations in Python and Pandas and ML Project Exploration
- Working with Files
- Serialization Techniques
- Use Pandas DataFrames
- Concurrency in Python
- Walking through Social Power NBA EDA and ML Project
2.2: AWS Cloud-Native Python for ML/AI
- 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
Screencasts (Can Be Watched from 1-4x speed)
Older Version of Python Fundamentals (Safari Version Is Newer)
Additional Topics
Python Programming Recipes
Managed ML and IoT
Software Carpentary: Testing, Linting, Building
Concurrency in Python
Cloud Computing-AWS-Sentiment Analysis
Recommendation Engines
Cloud Computing-Azure-Sentiment Analysis
Cloud Computing-AWS
Cloud Computing-GCP
Machine Learning and Data Science Full Jupyter Notebooks
Data Visualization
Seaborn Examples
Plotly
Creating a complete Data Engineering API
Statically Generated Websites
Deploying Python Packages to PyPi
Web Scraping in Python
Logging in Python
Conceptual Machine Learning
Linear Regression
Machine Learning Model Building for Regression
Mathematical and Algorithmic Programming
Optimization
Text
The text content of notebooks is released under the CC-BY-NC-ND license