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Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications
Computers can't LEARN... Right?!Machine Learning is a branch of computer science that wants to stop programming computers using a detailed list of commands to follow blindly. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are called algorithms.
In practice, they want to give computers the ability to Learn and to Adapt.We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people.
Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it.
Supervised Learning AlgorithmsK-Nearest NeighbourNaïve BayesRegressionsUnsupervised Learning Vector MachinesNeural NetworksDecision Trees
In practice, they want to give computers the ability to Learn and to Adapt.We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people.
Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it.
Supervised Learning AlgorithmsK-Nearest NeighbourNaïve BayesRegressionsUnsupervised Learning Vector MachinesNeural NetworksDecision Trees
- GenresTechnology
103 pages, Kindle Edition
Published June 21, 2017
122 people are currently reading
59 people want to read
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Displaying 1 - 8 of 8 reviews
August 14, 2017
I should preface this admittedly negative review by saying that I'm sure the author knows the subject far better than I do. And this work may serve as a suitable roadmap for the self-motivated scholar. However, I think it could've used a bit of work by an editor, though: spelling, grammar, typesetting for the maths, etc. It also could have used a more even treatment of each of the techniques, with respect to mathematical depth and worked examples.
July 31, 2018
Very well written, clean, well researched and explained.
I am writing a master thesis in neural networks and natural language processing. The book provided me with clean and easy to understand concepts that have cleared some unanswered questions about supervised learning methods. I look forward to read the sequel.
I am writing a master thesis in neural networks and natural language processing. The book provided me with clean and easy to understand concepts that have cleared some unanswered questions about supervised learning methods. I look forward to read the sequel.
November 4, 2017
Gave me the base line knowledge to apply to real world problems
Gave me the base line knowledge to apply to real world problems. It almost trivialized machine learning. Once you grasp the thought process behind machine learning it is not that difficult to understand
Gave me the base line knowledge to apply to real world problems. It almost trivialized machine learning. Once you grasp the thought process behind machine learning it is not that difficult to understand
July 26, 2017
Good introduction
If you have absolutely no prior knowledge on the subject this booklet is a good starter. However, I would expect a bit more illustration or examples.
If you have absolutely no prior knowledge on the subject this booklet is a good starter. However, I would expect a bit more illustration or examples.
November 11, 2017
Clear & walkthrough
I enjoyed very much, it's easy to read and understand, now I have been recharged with a new useful ideas behind learning machines!
I enjoyed very much, it's easy to read and understand, now I have been recharged with a new useful ideas behind learning machines!
May 6, 2018
To Surface Level
To anyone other than a data analysis neophytes, this book does not go into enough actual application or the kind of practical examples I was hoping for.
To anyone other than a data analysis neophytes, this book does not go into enough actual application or the kind of practical examples I was hoping for.
June 5, 2024
Good 100,000 Feet Primer
This is a great starter to start understanding a very complicated field. The author makes sure not to use jargon.
This is a great starter to start understanding a very complicated field. The author makes sure not to use jargon.
August 24, 2017
Introduction or Overview
Not really a book. Just an overview of the types of algorithms without going into detail or implementation. The book is too short and seems like it could fit as introduction to a much larger read. Feels like I didn't learn much.
Not really a book. Just an overview of the types of algorithms without going into detail or implementation. The book is too short and seems like it could fit as introduction to a much larger read. Feels like I didn't learn much.
Displaying 1 - 8 of 8 reviews