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[tutorial]A functional, Data Science focused introduction to Python

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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.

Do you find this free tutorial valuable! Please help spread the word:

  1. Star this Github Repo.
  2. If you have access to Safari, please like or comment my content on Safari.

PYTHON in ONE HOUR

Watch The Video Companions on YouTube

Learn Python in one hour!

Pragmatic AI Labs

Pragmatic AI Labs

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

Additional Related Topics from Noah Gift

  • 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

cloud-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)

  • Data Science Build Project
  • Data Science Build Project

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 Commandline Tools

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