Getting started
PyAI.info, also known as the Python AI Cookbook, is a website and online community focused on Applied AI with Python. It is created and maintained by experienced data scientists and data engineers.
Our primary audience is business professionals who are looking to harness AI tools and techniques. To fully benefit from our content, a foundational knowledge of Python and data science is recommended.
If you are new to Python or data science, don’t worry—this page is designed to guide you. Below, you’ll find a curated reading list to help you build your skills from the ground up, using both our site’s resources and carefully selected books.
Recommended Posts (coming soon)
On the coming weeks, we will publish the posts below to help people get started.
Getting Started with Python for Data Science
This post introduces you to setting up your Python environment, including installing Anaconda, Jupyter notebooks, and essential libraries like Pandas, NumPy, and Matplotlib. We also cover the basics of Python syntax, making it the perfect entry point for absolute beginners.
Introduction to Machine Learning with Python
An overview of machine learning principles, this post walks you through building your first model with scikit-learn. You’ll learn how to split data into training and test sets, train models, and evaluate their performance using metrics like accuracy and F1 score.
Exploring Data with Pandas
Pandas is a powerful tool for data manipulation and analysis. In this tutorial, you’ll learn how to load datasets, clean messy data, and conduct exploratory analysis. We also cover key functions like groupby, merge, and pivot, which are essential for working with real-world data.
Building Your First AI Model
Here, we guide you through the process of creating a simple AI model from scratch. Whether it’s a classifier or a regression model, you’ll learn how to preprocess data, define your model, and tune its parameters to improve performance.
Visualizing Data with Matplotlib and Seaborn
Data visualization is crucial for understanding trends and patterns. This post will help you master plotting libraries in Python, enabling you to create compelling visualizations to communicate your data-driven insights.
Natural Language Processing with Python
If you’re curious about working with text data, this post introduces you to basic NLP techniques. Learn how to tokenize text, build a simple text classifier, and explore advanced topics like sentiment analysis and named entity recognition.
Recommended Books
The following books come highly recommended. PyAI.info will include reviews, exercises and other related content. Where this content is available, links are given below.
Pandas Cookbook (3rd Edition)
By William And & Matthew Harrison. Published by Packt.
This book is a must-read for anyone serious about data science with Python. It covers essential Pandas functions and workflows, from basic data wrangling to advanced data manipulation techniques. It’s an ideal companion for those working on real-world data projects and who want to master the key data-handling skills required in AI.
Python Crash Course
By Eric Matthes. Published by No Starch Press
This is the go-to book for those new to programming. It offers a comprehensive yet accessible introduction to Python, with step-by-step projects to build practical skills. The book strikes the right balance between theory and practice, making it perfect for beginners who want to quickly move from basics to creating Python programs.
Automate the Boring Stuff with Python
By Al Sweigart. Published by No Starch Press
For those who want to immediately see the practical benefits of Python, this book shows you how to automate everyday tasks like organizing files, processing spreadsheets, and scraping websites. It’s excellent for beginners with no programming experience who want to use Python to make their work more efficient.
Beyond the Basic Stuff with Python
By Al Sweigart. Published by No Starch Press
This is the follow-up to Automate the Boring Stuff and is designed for learners who want to refine their Python skills. It delves into deeper concepts like object-oriented programming, writing tests, and debugging code. It’s ideal for those who are comfortable with the basics and ready to take their coding to the next level.
Dive into Data Science (No Starch Press)
By Bradford Tuckfield. Published by No Starch Press
This book provides a hands-on approach to learning data science, focusing on Python. It includes practical projects that help you understand data cleaning, visualization, and predictive modeling. It’s perfect for those who want to go beyond just learning Python and start applying it in the context of data science and AI.
Data Science from Scratch
By Joel Grus. Published by O’Reilly
If you want to understand the foundational mathematics and algorithms behind data science and machine learning, this book is for you. It walks through concepts like linear algebra, statistics, and machine learning from the ground up, making it an excellent resource for curious learners who want to dive deep into the theory behind AI.