Machine Learning for Dummies

by John Paul Mueller, Luca Massaron

Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data―or anything in between―this guide makes it easier to understand and implement machine learning seamlessly. Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Genres: Technology, Programming, Computer Science, Reference, Mathematics, Artificial Intelligence, Computers

410 pages, Paperback

Published January 1, 2016

Reviews

[Kristin Harley · Rating 5 out of 5]

Having a master's in Information Science and coding education and experience, I nevertheless approached this subject with some trepidation. Yet I came away thinking, "Is that all there is to it?" This is an excellent introduction! I never cared for the "for Dummies" part of this title series, yet I have never disappointed by any book I've read, and this one certainly was worth it.
Technology is not "magic." It requires people to create, program and maintain it. AI in particular needs humans to compile, clean and properly format the test data that will "teach" algorithms to perform data-deluged, repetitive tasks. It's not romantic nor mysterious. It's not going to "replace" librarians, doctors (a great deal of AI is now used in diagnostics), engineers, or teachers. It is going to free them from mundane tasks and impossible calculations by hand.
If you don't remember or have not been exposed to statistics and polynomial factoring, I still recommend this book for its coherent, commonsense explanations of the basis of machine learning.

[Daniel · Rating 4 out of 5]

I am fascinated by machine learning. I just have a hard time learning machine learning. When I read the word math I thought there is now way I could learn math so I am still not good at math. What is the point of what I am writing. I have no idea. I enjoyed reading about neural networks, R and python. I like the chapter about machine learning library's to learn. I have got a long way to go to become good at machine learning but, I think this book got me on my way.

[John Li · Rating 2 out of 5]

Just read the table of contents and use Wikipedia.

[Daniel · Rating 4 out of 5]

I also read the first edition of machine learning for dummies. I read the paper back version of this book not the kind edition.

I like that the new edition has all the code in python instead of like the first edition which had some examples in python and others in R. Though for this edition you can still get R code for the examples on the dummies website.

I want to read this book again because sure I read it but, after about halfway though the book I did not understand much of what I read. Not that its the authors fault. I can't remember much of what I read in the book. So I will have to read it again so I can write a better review and understand what I read better.

Its 2022 and I have read it better. I only wish I could understand everything I read betty. I don't think that was the fault of the authors thought. Machine learning is not easy for me to undrstand.

I liked chapter 7 which was about some of the math used for machine learning. It talks about scalars, vectors, and matrix's. I defiantly need to learn more about math.

I liked learning some history of some machine learning models like decision trees and neural networks. Defiantly interesting reading about different machine learning models I can use. I thought the discussion about preprocessing was through. So much to think about when preparing data for machine learning.

The 3 projects also looked interesting. The most interesting to me was recommending products and movies.

I did not run any of the code in the book because it took me long enough to read through the book. I may run the code some other time. I may not.

And I also liked chapter 22: machine learning packages to master. Looks like a lot of interesting packages for machine learning. One of my favorites was opencv.

I can't wait to learn more about machine learning. Hopefully I start learning more about it instead of telling myself one day I will get around to finally learning more about machine learning.

If you want to learn more about machine learning you might like machine learning for dummies 2nd edition or maybe you won't I don't know.

[Alexis Bauer Kolak · Rating 4 out of 5]

I'm not sure how much of that I actually understood, but it was fascinating for what I did get. While I would have liked more hands on examples I get why that's hard.

The thing I will take away from this book is that machine learning is not what most people think it is. You do not feed a computer data and it just spits out a solution. If you show a computer 10,000 pictures of an apple, it doesn't understand what an apple is, or what colors it can be, or that it's round. A computer learns that when certain pieces of data in a table or vector are run through an algorithm, and they exceed a certain value, you get a positive result. The computer does not tweak that algorithm itself; it provides scores and results that allow data scientists to adjust the algorithm and try again.

If you're well versed in Python, and understand some of the more advanced mathematics concepts, you'll probably be fine with this and get a good general sense of what's happening behind the scenes, but don't expect to get a tutorial on how to do this.

[MK · Rating 5 out of 5]

A good intro to ML for complete beginners. There was a few typos and some things could have been explained much better but these faults were overshadowed by the many great coding examples throughout the book. Other books are either too maths based or lacked the code examples that are actually needed to fully grasp this topic. The source code was easy to access and the authors guided the user how to implement the code in different notebooks. There is enough background knowledge in this book for one to go and build their own AI/ML model, and this book provides a good touch point to always refer back to whenever inevitable challenges will arise in such an endeavor.

[Paula · Rating 3 out of 5]

I thought this book was ok. It talked about real life applications of ML, which I was already familiar with. They do show code in Python but it would have been better if there were actual exercises/small projects to work on. The book doesn't get into the algorithms until chapter 10 where it starts talking about the Perceptron, Greedy Classification Trees and Naive Bayes. Prior chapters deal with some of the history and "dangers" associated with AI. Talks about the ML process such as cleaning and validation.

[Victor · Rating 1 out of 5]

I recommend this book if you are not from the field and want to have a better view of 'what is happening over there with this fancy AI stuff'. It gives a very broad view and correct the biggest problems someone might have with it. If you are willing to learn Machine Learning I'd suggest the Machine Learning course in Coursera first.

[Mustafa Salamov · Rating 3 out of 5]

Even though this books provides some good information about machine learning and how it works, I would not say that it is written for "dummies".

I had some understanding of Python, but still found this book a bit difficult to understand and skipped a few chapters.

[Ben · Rating 4 out of 5]

This was an excellent book on machine learning (ML).

As I have been coding some pretty advanced ML algorithms myself, this book gave me a good idea on some of the background behind ML activities.

I enjoy reading theory and computer science books, and this was a good book!

4.2/5

[Mark Ashworth · Rating 4 out of 5]

It's good. It kinda goes from 0 to 100 about halfway in though. Helps to have a basic understanding of math, stats and programming before reading this, or else you're probably going to get very lost.

[Shubham Sharma · Rating 2 out of 5]

They wasted almost 100 pages of the book on R and Python installation!

[Chaitat · Rating 2 out of 5]

Difficult to follow

[Ro · Rating 4 out of 5]

I need a dumber version for some of these topics

[Ryan Purvis · Rating 3 out of 5]

The book is good and covers the right things. I just didn’t find real use case enough.

[Delhi Irc]

Location: GG6 IRC
Accession No: DL030102