Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Python for AI: Data Science and Math for Machine Learning

Python for AI: Data Science and Math for Machine Learning

Dreisner, Thalric

Paperback

Series: Python for Ai: Learn Python Programming for Artificial Intelligence, Book 2

General ComputersProgramming

ISBN13: 9798275913644
Publisher: Independently Published
Published: Nov 24 2025
Pages: 550
Weight: 2.77
Height: 1.11 Width: 8.50 Depth: 11.00
Language: English
If you've ever looked at a messy spreadsheet and thought, There's no way this can turn into artificial intelligence, then this book is about to prove you wrong - and make you laugh while doing it.

Welcome to Python for AI: Data Science and Math for Machine Learning, the second book in my Mastering Artificial Intelligence with Python series - a journey designed to take you from curious coder to confident AI creator, one skill (and one bad joke) at a time.

You've already learned the basics of Python in Python for AI: Learn Python Programming for Artificial Intelligence, and now it's time to dive into the brainpower behind the bots - data science, math, and the beautiful chaos of real-world data. Because here's the truth: every great AI model starts with ugly data and a determined human who refuses to quit.

In this book, you'll uncover how raw data becomes machine intelligence. You'll learn to clean it, shape it, explore it, and find its hidden patterns - transforming noise into knowledge and spreadsheets into stories. Using Python's most powerful data science libraries - NumPy, Pandas, Matplotlib, and Seaborn - you'll discover how to manipulate, visualize, and analyze data like a pro.

We'll also roll up our sleeves and tackle the math that gives AI its superpowers. From statistics and probability to linear algebra, calculus, and optimization, you'll build the intuition behind how machines learn, adapt, and make decisions. Don't worry - I promise not to bore you with endless formulas. You'll learn through real-world examples, hilarious analogies, and a healthy dose of aha! moments.

You'll also explore:

  • The difference between structured, unstructured, and semi-structured data - and how to tame them all.
  • The secrets of Exploratory Data Analysis (EDA) and how to uncover insights hiding in plain sight.
  • Techniques for cleaning messy data, handling outliers, and building your first data pipelines.
  • The art of feature engineering - where data gets its glow-up before meeting machine learning.
  • How to prepare real-world datasets from sources like Kaggle and UCI Machine Learning Repository.
  • And how to connect everything you've learned to the next stage: machine learning itself.

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