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Machine Learning for Absolute Beginners: A Plain English Introductiom
With data collection on the rise machine learning is a hot topic. The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest.
In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would.
But as a machine, can consider many more scenarios and execute calculations to solve complex problems.
This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning.
This book provides a plain English introduction to the following topics:
- Artificial Intelligence
- Big Data
- Downloading Free Datasets
- Regression
- Support Vector Machine Algorithms
- Deep Learning/Neural Networks
- Data Reduction
- Clustering
- Association Analysis
- Decision Trees
- Recommenders
- Machine Learning Careers
This book has recently been updated following feedback from readers. Version II now includes:
- New Chapter: Decision Trees
- Cleanup of minor errors
In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would.
But as a machine, can consider many more scenarios and execute calculations to solve complex problems.
This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning.
This book provides a plain English introduction to the following topics:
- Artificial Intelligence
- Big Data
- Downloading Free Datasets
- Regression
- Support Vector Machine Algorithms
- Deep Learning/Neural Networks
- Data Reduction
- Clustering
- Association Analysis
- Decision Trees
- Recommenders
- Machine Learning Careers
This book has recently been updated following feedback from readers. Version II now includes:
- New Chapter: Decision Trees
- Cleanup of minor errors
181 pages, Kindle Edition
Published December 31, 2020
1175 people are currently reading
949 people want to read
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Displaying 1 - 30 of 66 reviews
January 8, 2018
A bit too brief and unstructured. You get a a quick overview of machine learning, big data, data analysis etc, but not more than what can be gathered from various online articles. I guess I feel the book lacks a bit of focus as it concludes with a career guide, which seems out of place compared to the rest. An clearer introduction/purpose with the book would have been good, but still a decent place to start, although further resources/references and an overview of the literature is missing.
May 16, 2023
Good introduction and referrence material
I have been bouncing around this ebook for some time trying to absorb as much as possible, and it's helped me to understand machine learning a lot. The author is good about guiding the reader towards other relevant peripheral knowledge. I recommwnd this to beginners as a good place to start, but for me, it's keeper as referrence material.
I have been bouncing around this ebook for some time trying to absorb as much as possible, and it's helped me to understand machine learning a lot. The author is good about guiding the reader towards other relevant peripheral knowledge. I recommwnd this to beginners as a good place to start, but for me, it's keeper as referrence material.
August 17, 2022
Exactly what I wanted, a very short book to help me build up a vocabulary before I move onto more extensive resources
July 7, 2020
A good book to pick, if you are looking for an introduction into the world of Machine Learning. But the second half of the book tends to get a bit too technical for the uninitiated. Overall, a good read. 3.5 stars!
April 26, 2020
First of all, this book is not just for absolute beginners. Often when I am taking a class that is going deep into a technique or algorithm, I get to the point where I cannot pick up my head and see why I am doing something, or where it fits in the machine learning lifecycle. Further, I come across a term that I know, but can't recall its purpose. This is great for that, and I think it should be in the library of everyone studying data science. It is a very short, to-the-point read that can also serve as a reference.
November 3, 2017
Excellent introduction to machine learning in which the author describes what machine learning is, techniques and algorithms, and future of & resources for machine learning learners.
The book is meant to provide an overview for the “absolute beginner” so that he understands what machine learning is all about. The author provides simple examples for each algorithm/technique to help explain it, but by no means he’s trying to teach the reader everything about that algorithm/technique. So this book is a good starting book for the topic prior to going further to read and study specialized books.
The book is meant to provide an overview for the “absolute beginner” so that he understands what machine learning is all about. The author provides simple examples for each algorithm/technique to help explain it, but by no means he’s trying to teach the reader everything about that algorithm/technique. So this book is a good starting book for the topic prior to going further to read and study specialized books.
December 15, 2018
Absoulutely great. I'd recommend definitely for a first approach and as introductory text for any AI/DL training / education. I think I am going to use it as consult book and even I might buy it in paper after having bought it on Kindle.
July 5, 2022
Great introduction!
If you are starting from scratch, as I am, this book is a great starting point. It introduces basic and important concepts and give you an idea where to go from there.
If you are starting from scratch, as I am, this book is a great starting point. It introduces basic and important concepts and give you an idea where to go from there.
August 15, 2020
Machine Learning For Absolute Beginners is poorly written and sloppily put together. While there is some valuable knowledge to be acquired, it is not well presented and explained. As a data scientist might say: the data in this book is very noisy.
The book cannot decide whether it wants to be a step-by-step guide or a reference text and, as a result, its structure and content make little sense. The author opens by describing a "toolset" framework for approaching ML and then proceeds to take up most of the middle part of the book discussing the theory behind various algorithm categories. (For some reason, a chapter about Bias & Variance is wedged into that section.) Then, the last part of the book gets suddenly very technical by providing a code sample in Python that uses a few of the techniques described previously. It would have been much better, in my opinion, if such code samples accompanied every chapter to showcase how each algorithm is used in practice.
Machine Learning is covered very unevenly. Most of the time, the author over-explains basic concepts and dives into very low level details, e.g. a mathematical formula, almost as if he tries to prove his credentials. At other times however, he glosses over some advanced topics with barely any explanation. Those passages read like Wikipedia articles and make me wish there were hyperlinks I could click on. Footnotes are used sparingly and are themselves badly written too: some refer to books or papers, while others are on obvious points that don't need clarification.
To be entirely fair, I finished Machine Learning For Absolute Beginners having learned some new things. But I can't say that this book is good value for money. There are much better resources online.
The book cannot decide whether it wants to be a step-by-step guide or a reference text and, as a result, its structure and content make little sense. The author opens by describing a "toolset" framework for approaching ML and then proceeds to take up most of the middle part of the book discussing the theory behind various algorithm categories. (For some reason, a chapter about Bias & Variance is wedged into that section.) Then, the last part of the book gets suddenly very technical by providing a code sample in Python that uses a few of the techniques described previously. It would have been much better, in my opinion, if such code samples accompanied every chapter to showcase how each algorithm is used in practice.
Machine Learning is covered very unevenly. Most of the time, the author over-explains basic concepts and dives into very low level details, e.g. a mathematical formula, almost as if he tries to prove his credentials. At other times however, he glosses over some advanced topics with barely any explanation. Those passages read like Wikipedia articles and make me wish there were hyperlinks I could click on. Footnotes are used sparingly and are themselves badly written too: some refer to books or papers, while others are on obvious points that don't need clarification.
To be entirely fair, I finished Machine Learning For Absolute Beginners having learned some new things. But I can't say that this book is good value for money. There are much better resources online.
July 25, 2019
Great book to start with machine learning! The author has provided an easy to understand overview of the ‘machine learning toolbox’. If you are interested in the field of A.I./machine learning but feel overwhelmed by the information available on this topic on internet, then this is the book to read. I quite liked the way the author started the book with first explaining differences amongst various data subfields- data mining, machine learning et cetera.
The only thing I miss in the book is a kind of visual summaries to give a one glance snap shot of topics explained.
The only thing I miss in the book is a kind of visual summaries to give a one glance snap shot of topics explained.
February 19, 2018
Good read
I'm hooked just a beginner but can't stop reading, transferable gave me a little edge on a few things I learned years back.
I'm hooked just a beginner but can't stop reading, transferable gave me a little edge on a few things I learned years back.
May 19, 2021
the book is very good at the start and the end, but the middle section of the book is very poorly written with a lot of unnecessary complex algorithms.
July 4, 2019
This serves the purpose if intention is only overview of topics. If one reads this with expectation with an expectation of learning whole machine learning then they will be disappointed. I think book should be titled as "Machine learning Overview".
Anyways, some basic knowledge/information is provided in this book. Those who have not googled much would benefit from it. Basic information are useful for answering in interview [although its in limited amount]. But still its okay book.
Read this one, if you just decided to start with machine learning study or searching for difference between big data, data mining, machine learning, AI, neural network etc. Yes, book is unstructured, written like topics /paragraphs.
Suddenly you will feel its over. I mean one should expect concrete information . I felt like ordering a pizza , it was good, I can feel the taste and smell. But as soon as I ate first bite, its over. :)
Anyways, some basic knowledge/information is provided in this book. Those who have not googled much would benefit from it. Basic information are useful for answering in interview [although its in limited amount]. But still its okay book.
Read this one, if you just decided to start with machine learning study or searching for difference between big data, data mining, machine learning, AI, neural network etc. Yes, book is unstructured, written like topics /paragraphs.
Suddenly you will feel its over. I mean one should expect concrete information . I felt like ordering a pizza , it was good, I can feel the taste and smell. But as soon as I ate first bite, its over. :)
September 12, 2023
O introducere de mică anvergură în ”Machine Learning”, care oferă câteva raze de lumină ce pătrund voalul de ignoranță în ceea ce privește subiectul. Cartea este bine structurată și ajută cititorul să înțeleagă de ce anume se ocupă ”Machine Learning”, prin ce se diferențează de AI și ”data mining”, care sunt instrumentele necesare cu care acesta trebuie să se înarmeze și câțiva algoritmi folosiți.
În general, stilul este unul clar, iar autorul încearcă să folosească un limbaj cât mai simplu și analogii care să sporească înțelegerea. Totuși pe alocuri există idei destul de încărcate și am simțit că lipsa unei baze teoretice nu mi-a permis să le înțeleg cu adevărat.
Partea de programare dă impresia a fi accesibilă, căci există multe librării care pun la dispoziție algoritmii și metodele necesare, dar pentru a calibra modelul cred că e nevoie de mai multă intuiție matematică. Cu toate acestea, cartea a făcut domeniul să pară abordabil și chiar interesant.
În general, stilul este unul clar, iar autorul încearcă să folosească un limbaj cât mai simplu și analogii care să sporească înțelegerea. Totuși pe alocuri există idei destul de încărcate și am simțit că lipsa unei baze teoretice nu mi-a permis să le înțeleg cu adevărat.
Partea de programare dă impresia a fi accesibilă, căci există multe librării care pun la dispoziție algoritmii și metodele necesare, dar pentru a calibra modelul cred că e nevoie de mai multă intuiție matematică. Cu toate acestea, cartea a făcut domeniul să pară abordabil și chiar interesant.
February 28, 2019
The book does it's best not to overwhelm the beginners at the same time, it doesn't disappoint as well by being shallow. The use cases are interesting, the methods & the mathematical means to derive the results aligned to the problem statement are well introduced. Short, good & helpful stuff for someone in the phases of building their passion for the field of data science. Don't get me wrong - keeping aside the fact that it is extremely well paying venture, but the it is worth the pursuit for only those who do intend to get intimate with bigger problems at a data level, discover insights we never knew and ultimately create an impact. In this scenario, one can become a useful data professional regardless of the fact that they never had the relevant background.
August 5, 2017
This is a very good, concise and to the point introduction to machine learning. Please note that when the author says it in the title, "Machine Learning for Absolute Beginners", he means it.
This is NOT going to give you insight into algorithms neither even is it going to briefly cover any practical aspects of those, but it WILL tell you what Machine Learning is, and what should/should not you expect before delving into the vast world of machine learning.
This is NOT going to give you insight into algorithms neither even is it going to briefly cover any practical aspects of those, but it WILL tell you what Machine Learning is, and what should/should not you expect before delving into the vast world of machine learning.
April 26, 2022
A short and easy to read book, really intended only for those for know nothing about ML to get an overview of what ML does, and a general idea of the different algorithms used.
While most of the information in the book could probably be gotten online from blog posts, it was nice to have a curated selection in one place.
As I was the target audience, I enjoyed and benefitted from this book, but if you already have some idea of what ML is, this book may be too simple for you.
While most of the information in the book could probably be gotten online from blog posts, it was nice to have a curated selection in one place.
As I was the target audience, I enjoyed and benefitted from this book, but if you already have some idea of what ML is, this book may be too simple for you.
June 18, 2017
Great book to enter the world of machine learning. Not only does the book explain the economic and social benefits of data science but it goes into the technicalities of regression analysis. Also has a small chapter on career pathways. Definitely an exciting and up-to-date intro to the next possible revolution in human history. Also a very short read and can be completed in less than 3 hours
June 27, 2017
Brief and healthy introduction to Machine Learning
I was hearing a lot about Machine Learning , AI , Data science, Bayes algorithm, Regression algorithm etc and to my surprise this book covers all of these topics with enough details such that I can now make further conscious details as how to proceed further.
This is goto book for newcomers like me.
I was hearing a lot about Machine Learning , AI , Data science, Bayes algorithm, Regression algorithm etc and to my surprise this book covers all of these topics with enough details such that I can now make further conscious details as how to proceed further.
This is goto book for newcomers like me.
July 7, 2017
Short but worth it
The book is short but I think that's it's strong suit. I suspect it's usefulness will wear off as this information becomes more foundational knowledge through my research into ML but as a beginner in the field, I find that having an easy to navigate reference book for the algorithms I need to learn to become an ML expert is very useful.
The book is short but I think that's it's strong suit. I suspect it's usefulness will wear off as this information becomes more foundational knowledge through my research into ML but as a beginner in the field, I find that having an easy to navigate reference book for the algorithms I need to learn to become an ML expert is very useful.
January 14, 2019
Might be too simple for the particular person who has already had the interests in Machine Learning (I mean, at least, they work in some related things). Yet, the author is very succesful to explain abstract concepts by friendly examples.
Even if the explanation is simple, they catch the most essential cores of machine learning concepts.
Highly recommend if you are truly absolute beginners
Even if the explanation is simple, they catch the most essential cores of machine learning concepts.
Highly recommend if you are truly absolute beginners
February 6, 2019
I got hooked on this book in a matter of minutes, the preface is very interesting however at times it felt unstructured but that is beside the point. It is a very Interesting book, I cannot believe it took me 1 and a half days to finish reading it. It has the theory without the scary maths formulas.
Great for beginners.
Great for beginners.
March 19, 2020
It's my first book to read and complete in the Machine Learning track and it's really a very good book for any beginner who wants to conquer this field and gain knowledge about it.
I recommend having some programming basics like dealing with python for full benefit.
Thanks for Oliver Theobald you helped me build my first machine learning model in my career and I hope it won't be the last one.
I recommend having some programming basics like dealing with python for full benefit.
Thanks for Oliver Theobald you helped me build my first machine learning model in my career and I hope it won't be the last one.
March 9, 2023
great entry point into learning. wording could have been simpler and it could have used more images. would have gotten the edition with pictures but couldnt find it for free online. if you do get this book, what helped me was looking up the confusing terms on Google Images. still don't understand what exactly "weight" is
May 30, 2023
Very Good
I suppose you can never hit perfection when it comes to such a variable topic like machine learning. Oliver Theobald did the job in getting across the basic concepts. Taking notes throughout reading this book and coming up my own explanations helped me further understand complicated concepts here.
I suppose you can never hit perfection when it comes to such a variable topic like machine learning. Oliver Theobald did the job in getting across the basic concepts. Taking notes throughout reading this book and coming up my own explanations helped me further understand complicated concepts here.
February 9, 2024
A crash course book on the methods of machine learning. It was a quick intro to the topic and despite being from 2017, it is still a good foundation. With so many modern tools that are plug and plug it is worth the quick read to understand the fundamentals. I am not sure it needed the python code at the end but it was handy for people looking to get deep into the technical side.
May 23, 2017
Great intro to Data Science
This is a good and quick read into the popular algos in Data Science. As the title suggests, it is a good intro and offers several resources to dive into the world of Data Science.
This is a good and quick read into the popular algos in Data Science. As the title suggests, it is a good intro and offers several resources to dive into the world of Data Science.
June 25, 2018
Great for people with no background in machine learning
Very easy to understand beginners book. Great for those interested in the subject and want something basic to get then started.
Very easy to understand beginners book. Great for those interested in the subject and want something basic to get then started.
November 22, 2020
Good for beginners, but!
The book is just as described, for absolute beginners, I think it has a bit deeper vision or some examples, quizzes, or exercises to help the reader reinforce the idea.
The book is just as described, for absolute beginners, I think it has a bit deeper vision or some examples, quizzes, or exercises to help the reader reinforce the idea.
January 31, 2022
Anyone who is looking for a quick read regarding the fundamentals of Machine Learning may opt for this book. However, it isn't suitable for people striving to gain a more mathematical perspective on the subject.
Displaying 1 - 30 of 66 reviews