Machine Learning in Action

Machine Learning in Action MOBI ↠ Machine Learning

Machine Learning in Action [PDF / Epub] ☉ Machine Learning in Action By Peter Harrington – Polishdarling.co.uk The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades Machine The ability to take raw data, access it, filter it, process Machine Learning ePUB Ù it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades Machine learning, the process of automating tasks once considered the domain of highly trained analysts and mathematicians, is the key to efficiently extracting useful information.


About the Author: Peter Harrington

There are several authors with this name on Goodreads.



10 thoughts on “Machine Learning in Action

  1. Andre Andre says:

    Want to know where, in Portland, OR, to park so that you can walk to the most strip clubs Yes, this is a real example in this book the data set consists of Magic Gardens, Mary s, Dolphin II, etc I kid you not As a result, I ll never forget the k Means algorithm.


  2. Rex Rex says:

    At first I just liked this book because it had some nice explanations about the basics of machine learning and I was interested in a general overview Then I encountered single value decomposition and latent semantic analysis and soon found that the Internet only contained dozens of horrible purely academic explanations about the underlying math that were basically impenetrable This book, on the other hand, had a very clearly worded walk through on a topic that is otherwise scarily difficult to At first I just liked this book because it had some nice explanations about the basics of machine learning and I was interested in a general overview Then I encountered single value decomposition and latent semantic analysis and soon found that the Internet only contained dozens of horrible purely academic explanations about the underlying math that were basically impenetrable This book, on the other hand, had a very clearly worded walk through on a topic that is otherwise scarily difficult to find


  3. Sebastian Gebski Sebastian Gebski says:

    Unfortunately, I can t provide a discrete rating Due to following reasons usually when things started to get interesting for a particular algorithm method instead of diving into mathematics behind method, author was jumping into Python for majority of people it really makes sense developers are not mathematicians, but I personally dislike Python, so I d rather prefer somedetails so I couldeasily map them to other programming language but I can t blame anyone but myself Unfortunately, I can t provide a discrete rating Due to following reasons usually when things started to get interesting for a particular algorithm method instead of diving into mathematics behind method, author was jumping into Python for majority of people it really makes sense developers are not mathematicians, but I personally dislike Python, so I d rather prefer somedetails so I couldeasily map them to other programming language but I can t blame anyone but myself author didn t hide the fact that code samples are in Python due to other stuff I was doing in the meantime, I wasn t able to do any practice alongside reading the book and it s a problem, because it s one of the books you can t just read without putting your hands on code You have to actually touch the described algorithms methods to make some sense out of them good example Adaptive Boosting sadly I ve failed again I didn t have time for that Anything else to add Well, apart from what I ve written above 1 There s a section 3 chapters about Unsupervised Learning it s great, because I haven t seen many practical non pure statistics books on the topic recently2 Some chapters seem a bit out of topic the Hadoop stuff, Tkinter.To summarize I can t give a star rating And I have a feeling I ll be coming back to this book in a bitconvenient time


  4. Kai Jiang Kai Jiang says:

    Read this when second year of my Master s This book islike a intro or shallow summary for the huge Machine Learning world, yet it comes with easy understanding Python code and just the right amount of math background so you can get the sense very quickly.


  5. Kursad Albayraktaroglu Kursad Albayraktaroglu says:

    It s been three months since my wife asked me why I wasalways reading the same book with the axe guy on the coverbut I am finally done with this book It took a lot of effort to finish it by working out all the examples and exercises but I think it was well worth it I personally think Harrington s book strikes a very good balance between mathematical and programming aspects of ML and would be a great introductory book for anyone with working knowledge of Python and preferably some backgr It s been three months since my wife asked me why I wasalways reading the same book with the axe guy on the coverbut I am finally done with this book It took a lot of effort to finish it by working out all the examples and exercises but I think it was well worth it I personally think Harrington s book strikes a very good balance between mathematical and programming aspects of ML and would be a great introductory book for anyone with working knowledge of Python and preferably some background in statistics Since the book heavily depends on Python code examples and exercises, it may not be the best choice for a non programmer the author prefers to explain many complex subjects in code, and you will not understand the material if you skip the coding examples It looks like Paul Wilmott s new book and Andriy Burkov s The Hundred Page Machine Learning Book are better suited for themathematically oriented ML learners For anyone approaching ML from the software development side, Harrington s book is highly recommended


  6. Suhrob Suhrob says:

    A book caught in the uncanny valleyHarrington strives to give introduction to basic machine learning topics and algorithms by developing them from scratch in python using numpy and matplotlib but not scipy scikit learn This way he indeed givesinsight than just a completely black box approach but nowhere near as much understanding as a proper mathematical treatment of the algorithms On the other hand the implementations are rudimentary and in fact for all practical purposes one would A book caught in the uncanny valleyHarrington strives to give introduction to basic machine learning topics and algorithms by developing them from scratch in python using numpy and matplotlib but not scipy scikit learn This way he indeed givesinsight than just a completely black box approach but nowhere near as much understanding as a proper mathematical treatment of the algorithms On the other hand the implementations are rudimentary and in fact for all practical purposes one would just use any state of art library instead the introduced basic algorithms So the book is neither theoretical too shallow for that nor practical not showing you any of the libraries actually used in practice.It is written with earnestness and withcare than what s typical for similar programming handbooks, but I have no idea who I could recommend it due its neither nor nature


  7. Avinash K Avinash K says:

    The book is good for the summary information and to get your feet wet The writing is lucid and not intimidating So one can start wrapping one s head around the ideas On the flip side it lacks the required mathematical background So the book a starter, a decent starter A good book to have along side is The Elements of Statistical Learning statweb.stanford.edu tibs ElemStatLe The book is good for the summary information and to get your feet wet The writing is lucid and not intimidating So one can start wrapping one s head around the ideas On the flip side it lacks the required mathematical background So the book a starter, a decent starter A good book to have along side is The Elements of Statistical Learning statweb.stanford.edu tibs ElemStatLe


  8. Kiril Kirilov Kiril Kirilov says:

    I personally think that the Coursera s course is much better way to inform the unprepared mind about the marvelous world of Machine Learning algorithms.


  9. Tao Tao says:

    Too shallow..


  10. Cheogm Cheogm says:

    I really love this book but I can not give it a 5, because to fully understand the topics I had to study from other media the mathematics it is not like math and code for example sometimes the author develops an algorithm which hassteps from what is develop and what is presented as the equation By the other hand it is a very good way to start the topic I am pretty sure that after you read this book you will understand at Lest when people talk about the topics in a very good and deep way.


Leave a Reply

Your email address will not be published. Required fields are marked *


10 thoughts on “Machine Learning in Action

  1. Andre Andre says:

    Want to know where, in Portland, OR, to park so that you can walk to the most strip clubs Yes, this is a real example in this book the data set consists of Magic Gardens, Mary s, Dolphin II, etc I kid you not As a result, I ll never forget the k Means algorithm.


  2. Rex Rex says:

    At first I just liked this book because it had some nice explanations about the basics of machine learning and I was interested in a general overview Then I encountered single value decomposition and latent semantic analysis and soon found that the Internet only contained dozens of horrible purely academic explanations about the underlying math that were basically impenetrable This book, on the other hand, had a very clearly worded walk through on a topic that is otherwise scarily difficult to At first I just liked this book because it had some nice explanations about the basics of machine learning and I was interested in a general overview Then I encountered single value decomposition and latent semantic analysis and soon found that the Internet only contained dozens of horrible purely academic explanations about the underlying math that were basically impenetrable This book, on the other hand, had a very clearly worded walk through on a topic that is otherwise scarily difficult to find


  3. Sebastian Gebski Sebastian Gebski says:

    Unfortunately, I can t provide a discrete rating Due to following reasons usually when things started to get interesting for a particular algorithm method instead of diving into mathematics behind method, author was jumping into Python for majority of people it really makes sense developers are not mathematicians, but I personally dislike Python, so I d rather prefer somedetails so I couldeasily map them to other programming language but I can t blame anyone but myself Unfortunately, I can t provide a discrete rating Due to following reasons usually when things started to get interesting for a particular algorithm method instead of diving into mathematics behind method, author was jumping into Python for majority of people it really makes sense developers are not mathematicians, but I personally dislike Python, so I d rather prefer somedetails so I couldeasily map them to other programming language but I can t blame anyone but myself author didn t hide the fact that code samples are in Python due to other stuff I was doing in the meantime, I wasn t able to do any practice alongside reading the book and it s a problem, because it s one of the books you can t just read without putting your hands on code You have to actually touch the described algorithms methods to make some sense out of them good example Adaptive Boosting sadly I ve failed again I didn t have time for that Anything else to add Well, apart from what I ve written above 1 There s a section 3 chapters about Unsupervised Learning it s great, because I haven t seen many practical non pure statistics books on the topic recently2 Some chapters seem a bit out of topic the Hadoop stuff, Tkinter.To summarize I can t give a star rating And I have a feeling I ll be coming back to this book in a bitconvenient time


  4. Kai Jiang Kai Jiang says:

    Read this when second year of my Master s This book islike a intro or shallow summary for the huge Machine Learning world, yet it comes with easy understanding Python code and just the right amount of math background so you can get the sense very quickly.


  5. Kursad Albayraktaroglu Kursad Albayraktaroglu says:

    It s been three months since my wife asked me why I wasalways reading the same book with the axe guy on the coverbut I am finally done with this book It took a lot of effort to finish it by working out all the examples and exercises but I think it was well worth it I personally think Harrington s book strikes a very good balance between mathematical and programming aspects of ML and would be a great introductory book for anyone with working knowledge of Python and preferably some backgr It s been three months since my wife asked me why I wasalways reading the same book with the axe guy on the coverbut I am finally done with this book It took a lot of effort to finish it by working out all the examples and exercises but I think it was well worth it I personally think Harrington s book strikes a very good balance between mathematical and programming aspects of ML and would be a great introductory book for anyone with working knowledge of Python and preferably some background in statistics Since the book heavily depends on Python code examples and exercises, it may not be the best choice for a non programmer the author prefers to explain many complex subjects in code, and you will not understand the material if you skip the coding examples It looks like Paul Wilmott s new book and Andriy Burkov s The Hundred Page Machine Learning Book are better suited for themathematically oriented ML learners For anyone approaching ML from the software development side, Harrington s book is highly recommended


  6. Suhrob Suhrob says:

    A book caught in the uncanny valleyHarrington strives to give introduction to basic machine learning topics and algorithms by developing them from scratch in python using numpy and matplotlib but not scipy scikit learn This way he indeed givesinsight than just a completely black box approach but nowhere near as much understanding as a proper mathematical treatment of the algorithms On the other hand the implementations are rudimentary and in fact for all practical purposes one would A book caught in the uncanny valleyHarrington strives to give introduction to basic machine learning topics and algorithms by developing them from scratch in python using numpy and matplotlib but not scipy scikit learn This way he indeed givesinsight than just a completely black box approach but nowhere near as much understanding as a proper mathematical treatment of the algorithms On the other hand the implementations are rudimentary and in fact for all practical purposes one would just use any state of art library instead the introduced basic algorithms So the book is neither theoretical too shallow for that nor practical not showing you any of the libraries actually used in practice.It is written with earnestness and withcare than what s typical for similar programming handbooks, but I have no idea who I could recommend it due its neither nor nature


  7. Avinash K Avinash K says:

    The book is good for the summary information and to get your feet wet The writing is lucid and not intimidating So one can start wrapping one s head around the ideas On the flip side it lacks the required mathematical background So the book a starter, a decent starter A good book to have along side is The Elements of Statistical Learning statweb.stanford.edu tibs ElemStatLe The book is good for the summary information and to get your feet wet The writing is lucid and not intimidating So one can start wrapping one s head around the ideas On the flip side it lacks the required mathematical background So the book a starter, a decent starter A good book to have along side is The Elements of Statistical Learning statweb.stanford.edu tibs ElemStatLe


  8. Kiril Kirilov Kiril Kirilov says:

    I personally think that the Coursera s course is much better way to inform the unprepared mind about the marvelous world of Machine Learning algorithms.


  9. Tao Tao says:

    Too shallow..


  10. Cheogm Cheogm says:

    I really love this book but I can not give it a 5, because to fully understand the topics I had to study from other media the mathematics it is not like math and code for example sometimes the author develops an algorithm which hassteps from what is develop and what is presented as the equation By the other hand it is a very good way to start the topic I am pretty sure that after you read this book you will understand at Lest when people talk about the topics in a very good and deep way.


Leave a Reply

Your email address will not be published. Required fields are marked *