Machine Learning For Dummies, 2nd Edition
- 7h 42m
- John Paul Mueller, Luca Massaron
- John Wiley & Sons (US)
- 2021
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.
In this Book
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Introduction
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Getting the Real Story about AI
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Learning in the Age of Big Data
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Having a Glance at the Future
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Installing a Python Distribution
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Beyond Basic Coding in Python
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Working with Google Colab
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Demystifying the Math behind Machine Learning
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Descending the Gradient
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Validating Machine Learning
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Starting with Simple Learners
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Preprocessing Data
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Leveraging Similarity
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Working with Linear Models the Easy Way
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Hitting Complexity with Neural Networks
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Going a Step beyond Using Support Vector Machines
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Resorting to Ensembles of Learners
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Classifying Images
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Scoring Opinions and Sentiments
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Recommending Products and Movies
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Ten Ways to Improve Your Machine Learning Models
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Ten Guidelines for Ethical Data Usage
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Ten Machine Learning Packages to Master
