Machine Learning For Dummies

Machine Learning For Dummies
  • eBook:
    Machine Learning For Dummies
  • Author:
    John Paul Mueller, Luca Massaron
  • Edition:
    2 edition
  • Categories:
  • Data:
    February 9, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    464 pages
  • Format:
    PDF, ePUB

Book Description
Your comprehensive entry-level guide to machine learning 
While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android―as in the movie Ex Machina―it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit- scoring, building accurate and sophisticated pricing models―and way, way more. 
Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn’t assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying―and fascinating―math principles that power machine learning but also shows that you don’t need to be a math whiz to build fun new tools and apply them to your work and study.  
  • Understand the history of AI and machine learning 
  • Work with Python 3.8 and TensorFlow 2.x (and R as a download) 
  • Build and test your own models 
  • Use the latest datasets, rather than the worn out data found in other books 
  • Apply machine learning to real problems 
Whether you want to learn for college or to enhance your business or career performance, this friendly beginner’s guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that’s impacting lives for the better all over the world.


Part 1: Introducing How Machines Learn
Chapter 1: Getting the Real Story about AI
Chapter 2: Learning in the Age of Big Data
Chapter 3: Having a Glance at the Future

Part 2: Preparing Your Learning Tools
Chapter 4: Installing a Python Distribution
Chapter 5: Beyond Basic Coding in Python
Chapter 6: Working with Google Colab

Part 3: Getting Started with the Math Basics
Chapter 7: Demystifying the Math Behind Machine Learning
Chapter 8: Descending the Gradient
Chapter 9: Validating Machine Learning
Chapter 10: Starting with Simple Learners

Part 4: Learning from Smart and Big Data
Chapter 11: Preprocessing Data
Chapter 12: Leveraging Similarity
Chapter 13: Working with Linear Models the Easy Way
Chapter 14: Hitting Complexity with Neural Networks
Chapter 15: Going a Step Beyond Using Support Vector Machines
Chapter 16: Resorting to Ensembles of Learners

Part 5: Applying Learning to Real Problems
Chapter 17: Classifying Images
Chapter 18: Scoring Opinions and Sentiments
Chapter 19: Recommending Products and Movies

Part 6: The Part of Tens
Chapter 20: Ten Ways to Improve Your Machine Learning Models
Chapter 21: Ten Guidelines for Ethical Data Usage
Chapter 22: Ten Machine Learning Packages to Master

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