laptak2003/AmazonDataScience huggingface dataset:
The book definitely provides the ability to do data analysis from scratch. However, I took that to mean it would provide me with the basic knowledge to do data science as its done in production. What you want find hardly anywhere in this book is a single discussion of how to use pandas, scikit-learn, numpy, matplotlib, or any of the other tools used by today's data scientists.It does provide you with the basics of how the analysis is done and the math behind a bunch of machine learning models. Unfortunately there's definitely a high expectation of math knowledge in this book, starting with at least Linear Algebra through Calculus.So, while I think this book had value, it was very different from what I was expecting. I think the target audience for this book is more an academic looking to apply their knowledge to a data science realm, and not so much a programmer wanting to learn how to actually implement machine learning or data analysis in code.
Took some for the delivery. But the product is awesome
High quality. Outstanding service.
It a good overview book, however it would be good if difficult topics would be described without using domain specific terms, which assumes you already have ML knowlage. If the terms are used, they should be defined upfront. If variuos specific methods are mentioned, it would be good to provide a comparison and overview of those.  I general it is good book, which I would recommmend reading as a starting point for its structured approach.
I want to say that this book is the perfect size at around ~150 pages if you exclude the exercises at the back. In my opinion, It's balanced with text and figures/graphics and provides a high level overview of concepts, models, algorithms, etc.The book itself is a fairly quick read and can be tackled in an afternoon if it keeps your interest. There are a wealth of additional resources the book provides in both footnotes and appendices. It doesn't waste it's time with understanding the fundamental mathematics and instead leverages the use of publicly available Python libraries. The math of Machine Learning can easily be intimidating and I commend the authors from shying away from it.
The book is indeed written for beginners. The problem is that is it written very unclearly. In most cases you need to have a good idea of what the author is trying to describe in order to understand it. Try, for instance reading the section about logistic regression, and I am almost sure you won't understand it. The subject is actually simple and you will be better off just reading a Wikipedia article about it.In addition the are multiple typos that had to be caught by a corrector and some ridiculous cases when the author writes a formula, for instance 1/(1+e^(-x)) and spells is our in words "one divided by the sum of one and exponent â€¦.".Use this book only if you want to get a vocabulary of machine learning and try to get the actual info elsewhere.Disclaimer: I am an experienced software developer with an advanced degree in math
This is the first Machine Learning book Iâ€™ve read that didnâ€™t rocket right past me in the third chapter. The author knows their stuff, but doesnâ€™t mind slowing down and explaining what it all means in a well thought out way.
Happy to offer this book a glowing endorsement to beginners. Nothing in this book is intimidating. I was worried that I might get lost halfway through after the author started introducing the various algorithms. But no, this is where the book got significantly better. The visual examples and clear instructions made the rest of the book a breeze to follow. Although it was easy to follow, I donâ€™t feel like I lost out on technical content. For a high-level introduction, it was concise and detailed.In terms of constructive feedback to the author, I offer the following suggestions: - Extend the chapter on ANN. I would have liked to have seen some full examples as with the previous algorithm chapters.- No mention of face recognition? This is an aspect of ML I think a lot of readers will be interested in discovering more. This I assume would slot into the chapter with ANN.
New to ML but have done programming in previous jobs. Found this book extremely helpful. Gives a great introduction while instigating your curiosity to explore and learn more. Love it. Would request the author to write a sequel.
Don't buy it. Reads like a high-schooler's essay on some subject they superficially resesrched online. Very little real substance. Scary that this person is a (presumably) professional ML coder somewhere.
I would like to tell everybody, this two books are really great for who really want to jump in machine learning field. Lots of real world scenario, prefect example is one of the best features of these books.I really enjoy and learn lots of basic things to start my career in Machine Learning.
This book was written in plain English and really meant for people without a lot of Math background to understand. Unlike other books such as "the 100 page ML book" which is full of math, this book explains the concepts very well, without using a whole lot of math. This book is NOT for those who are serious about digging into data science or machine learning. But it's good for everyone else who wants to get a high level overview of what ML/Data science is about.
Attention to those who might pick up this book without having a strong background in data science or mathematics--large chunks of this book will probably be a slow slog for you, as it was for me.I am very surprised this book is rated so highly.  There are so many concepts/things thrown out there as if they were common knowledge, when they're not. By the point the book starts diving into coding basics around Chapter 13, I realized I needed to find another, better book. I've found free online resources and an online course that have made Python much more digestible.
Great intro to the subject in plain language. Check out the authors Stats for Absolute Beginners too!
This book gives a fantastic overview and helps while studying a related book on python.
This is not so much a product review as a complaint.  Several attempts to download to my Kindle failed, although Amazon claims it was delivered. I asked for a refend, etc. If I order again will the same happen?
This book left me feeling satisfied, yet was not overwhelming to read. Would reccomend fpr anyone wanting to know more about the workings of machine learning without having to have an advanced math or programming background.
If you find yourself questioning terms and approaches while taking a machine learning course then this is the book that will help filling in those details.
Good introductory book. I wish it had more examples though!
The author takes a very complex topic and makes it extremely user-friendly.  Very well done!
I chose 5 stars because this book is clear and concise. I would highly recommend this book for people who know a little about AI, but want to have a slightly more detailed overview.
This ebook has helped me progress my baseline understanding of machine learning. The concepts are explained in a smooth fashion with the right mix of statistical terms.
This is what I needed for a quick introduction to the subject.
The first book on a challenging topic that Iâ€™ve been able to fully understand all the way through the end of the book. Oliver does a fantastic job translating the difference between the most popular machine learning algorithms and the ways in which each one of them works. I did have to read each section multiple times until I fully grasped the concept, if you do the same you will have a solid starter foundation upon which to build upon to start your own research.
I knew next to nothing about machine learning and artificial intelligence. I am a now-retired physician wanting to have hans-on knowledge on these subjects. Very readable book! Easy to follow and to test yourself on your own computer.
Just received this book today and saw that there were missing text that make it look like a blank column on the even number pages for all paragraphs from page 2 to page 76.I'm sure this is an Amazon printing issue.This rating is not on the content of the book. I will rate it once I receive a readable replacement.....UPDATE....Recieved replacement book one day early. All of the printing looks great and returning the miss printed book through UPS was seamless. Thanks.
For a beginner like me, this was a very good introduction. Would have loved a bit more detail in each section, and the python appendix at the end is a waste of space, but other than that it was good.Also, on Kindle it had a bunch of random highlighted paragraphs.
I have been reading some ML books. This is one of my favorites. It is clear, concise. A great source for beginners.There are many terms discussed, also, some exercises.I wish there could be more exercises, but it is a nice step to start.There are many recommendations at the end of the book. I recommend that if you want some good base in ML, take a course, and use these books as a reference or to clear some topics. A must read for a beginner!
Book is really basic.If you feel like you know what machine learning is then you should already skip this book
I will need a second pass on the book but I think it gave a great introduction to the world of machine learning. Not boring at all!
Clear and concise introduction to ML. If you donâ€™t have a background in data science, I wouldnâ€™t let this stop you from picking up this book. My own expertise is in mobile web development, which is outside the scope of this book title, but I had no problems going through it.
I am an experienced professional but a novice in ML. This book is awesome, and provides the fundamentals to build upon, for grasping the topics easily in future readings of more dense books. Read it!
Excellent book for an absolute beginner who wants to understand the terms and get introduced to the subject. Would definitely recommend to all beginners.
Really really simple and nice.Very good..If you want one, get it. Just make sure you know that you can finish it in two nights.
Easy to understand, good for people that want to know machine learning
Amazing book for someone who may understand advanced statistics and need to make a connection with machine learning. Bravo to Oliver! I'm buying his other books
Really loved reading this book. The book covers all major aspects of machine learning with just the right amount of details. Very good for anyone who wants to know the basics.
Nice level of detail for someone looking for a useful tangible introductory understanding of key elements of machine learning.
Provides a quick overview of the key concepts. Though you would still need to get some hands on exercise outside of this to fully grasp the main ideas.
Nice starter book and helps you point in various direction for more detailed information in later readings.
Easy to understand for beginners or anyone at any level wants to refresh on ML. I would highly recommend for data analysts and aspiring data scientists
Great book for newbies to machine learning. You will get more from the book if you have a some knowledge of python.
Not something that would help those trying to build their ML domain knowledge.
Outputs of traditional computer programs are pre-defined by the programmer, machine learning uses data to build a decision model. Relationships and patterns in the data are deciphered via probabilistic reasoning, trial and error, and other  computationally-intensive techniques - output is determined by input data. Its predictions improve based on experience/additional data.Example - a self-learning model for detecting spam email. It first learns to flag emails with suspicious subject lines and body text containing keywords correlating highly with spam messages flagged by past users. Indicators may include words like dear friend, free, invoice, PayPal, Viagra, casino, payment, bankruptcy and winner. However, adding irrelevant data can be counter-productive.In machine learning, input data is typically split into training and test data. Modifications are then made - eg. payments@paypal.com should be excluded from filtering, or if in the original data the system would be trained to avoid that error. Machine learning incorporates several hundred statistical-based algorithms.Supervised learning imitates our own ability to extract patterns from known examples and use that insight to engineer a repeatable outcome. Eg., rather than speculate or create a unique process for manufacturing cars, Toyota created its first vehicle prototype after taking apart a Chevrolet in the corner of the family-run loom business. Input data are referred to as the independent variable, output data are called the dependent variable. Dependent variables might be coordinates for a rectangle around a person in a digital photo, the price of a house, or class of an item (sports car, family car). Independent variables supposedly impact the dependent variable - possibly pixel colors, size/location of a house, specifications of the car. (Linear, logistic, non-linear regression analysis, decision trees, k-nearest neighbors, neural networks, support vector machines are examples.)In supervised learning 'spam detection,' if the word 'benefit' is in the title, there's a 90% chance the email is spam, 'account number xxxxxx' in the content a 10% chance, more than 200 sent from one source at a time - a 60% chance, accounts with a less than 10% response rate - 70% chance. A score is calculated/email.However, with a large number of data fields it is no longer feasible to operate supervised learning with humans.
Se entiende muy bien, realmente para principiantes
Its as it says an intro to ML
Simple to understand and a great starting point for ML. I am now ready for much more advanced ML stuff.
Itâ€™s a good reading for the beginners of machine learning.
This provides an excellent overview of the major techniques. It's definitely worth reading.It could've been a little more specific at points, though. In particular, it doesn't really contain a lot of details on how to implement the techniques it describes, but I suppose that that would've required a much longer book.
This book answered all of the questions I had when speculating about Machine Learning. Next course of action is the Coursera Andrew Ng course.
This book is ideal for people first coming into ML looking for a clear high-level introduction. Having said that, the book is also quite practical too, and I had good fun going through the example exercises to build regression lines and scrub datasets.
A great intro to the topic with hands on application and suggestions for further learning. Thank you for writing this book
I'm a senior developer who has struggled with ML concepts in the past despite a strong math background. I was optimistic that a book for total laypeople would help, but as someone who learns mainly by doing, I found it just wasn't hands-on enough. The information was good, but the lack of worked examples or exercises left me feeling like I hadn't learned all that much. I'd say if you're someone who can read information and absorb it, this could be a great introduction to the subject. But if you're like me and need a sandbox, you might want to keep looking.
I enjoyed the readability of this book. The focus is on how to use and understand ML rather than the math behind the algorithms. Was an enjoyable read.The conclusion of his book could offer suggestions on further readings. There are dataset and online course suggestions. I would like to see a recommendation on where to go next for additional depth.Overall I am very pleased with this book. Previously I have been overwhelmed with the math and this helped draw me back out a few steps to better understand the concepts.
Over the past few months I have started getting into machine learning, and I have been taking courses on Udemy. Note, I have a background in engineering, so my math and basic programming are okay. But machine learning can feel big and complex to a novice. This book really helped me with cementing the various ideas and concepts covered in machine learning. A lot of this material can be found online, but the authored has done a good with organizing the material.
I didn't know anything about machine learning and algorithms used for it. I attended a lot of sessions to understand machine learning but this book is a very good start to machine learning. Love it. It teaches to set-up environment and work with a sample dataset
I liked the range of topics and examples that were described but some concepts, especially the types of algorithms, still seemed a little advanced for an â€œabsolute beginnersâ€ book.  For me, I had to read some sections multiple times to grasp the concept.I did like the last couple of chapters that showed how to work with a dataset to run through a machine learning problem.  It was written well with good step by step procedures.
I think this is a great kickstarter or review for someone who wants to learn the basics, not alot of math or STEM knowledge needed, but covers the basics and can be read in 1 night !
Not an expensive book, very clear in its definitions and very easy to read. I just received it and I am nearly a third through the book.  I am really enjoying this read!
Excellent overview for someone just starting to learn about ML. Covers the basics and a good introduction to the terms used in M.
Overall very well paced.  Great book to get a overarching view of machine learning. Simple language through out to explain some of the complex concepts. I thoroughly enjoyed reading.
The book it's a pretty good introduction to the machine learning field as the name suggests. It's enough to give you a quick grasp of some of the most used concepts in the ML/deep learning field. Nevertheless, the author also explains the pros and cons of both methodologies and when they are useful or a complete waste of time/money.
Simple. Straightforward. High level explanations of deep subjects. Just enough math to have it make sense, but simplified enough that any layperson could understand without much struggle.
This book has helped in clearing up some of my obviousness to machine learning. However, the chapter on decision trees and gradient boosting needs some more content. Nevertheless, awesome book
A good introduction and reference of toolkits needed to begin a rudimentary machine learning program. For the most part, the details provided are just enough to allow most readers to grasp these concepts.
It's kind of interesting, when you hear about machine learning and AI on podcasts and on TV, it always sounds so futuristic and abstract for us 'normal' folk. But this book helped me see it might not be so unattainable to get into this field. Yes, you need to have programming skills and a strong grasp of classical statistics but these are useful skills to have in a data-minded world anyway. I will leave it a few days before I decide to dive deeper into ML, but this book has certainly inspired me to look at it more closely as a potential hobby or career tangent.
If you're liking for a baseline on the ML subject, Theobald delivers several examples without the overwhelming math. If your in the AI field, this is common ground.
Nice understanding of basic concepts used in machine learning. Good starting point for beginners interested in the fieldf of data science
Good book for beginners, minimal math involved. Goes over 1 algorithms step by step with Python at the end. I might even buy a paperback to keep at the house
Great intro to machine learning. All around a good book to get a high level understanding in a practical manner
Read it over the weekend and it was very insightful.
not easy reading and not for beginners .
The book is an introduction into machine learning techniques which covers many of the topics without going into many detailed mathematical explanations.
This book covers the basics through implementation of simple machine learning. It was entertaining and highly educational. I especially appreciated the effort made by the author to walk a newbie through installation of a new programming environment and then using it to produce something useful. After reading this book, I'm ready to start my first ML experiment.
This wasn't exactly what I was thinking it was, but it turned out to be something that I feel I needed. Happy that I bought it anyway.
The book is an introduction into machine learning techniques which covers many of the topics without going into many detailed explanations.
I'm an R user and so the Python code sections weren't of much interest to me; but those sections are small. Most of the book deals with description of machine learning techniques rather than examples. I think this is a great quick read to introduce machine learning, and that it could serve as a good reference book, or as a springboard for more in depth reading.
This is the best machine learning book I have ever read!!! The author breaks things done into simple easy to understand components. I now have a better understanding of what the ML engineers are discussing.
In-depth knowledge for the absolute beginner. Has me up and running with basic algorithms in a few hours. Highly recommended.
This book provides very good overview of ML to beginners and touch base on majority of the key ML algorithms and concepts. Code sample towards the end gives a basic hands-on experience and understanding of the model, fit and test methodology. Highly recommended.
Easy to read and informative. Perfect intro
Good broad guide of the process of machine learning, can review in future. Will refer to this book later in the year.
good hand-on example
Another quick & easy read from this author. No Complaints.
1) From what I have seen so far, this is the best second book to read if you are an absolute beginner (the best first book would be Numsens by Ng and Soo). Theobald's book is more advanced, contains more details, and gives you a good foundation to start actually practicing machine learning on a computer as the next step.2) The reviews suggest that the first edition suffered from spelling mistakes and occasional glitches in the quality of writing. I have not read that edition myself, but this second edition is well written, and I found only one small mistake.3) Finally, I want to point out that this book, in my opinion at least, genuinely deserves all 4 & 5-star marks given by reviewers (sadly, the abuse of reviewing system by the deliberate 5-star flooding seems to be on ascendancy).
The author has a lot of knowledge on forecasting and how to use it in the specific domain, but also a perfect introduction for forecasting in general!
I am a supply chain practitioner with professional experience in demand planning and distribution strategy. I continually look for opportunities to up-skill and came across Data Science For Supply Chain Forecast. This book was a perfect primer in using modern forecasting techniques in a supply chain setting.The structure of the book allows for you to build models in progression of complexity and power. Start small and improve model performance. Nicolas begins with classical models before diving into machine learning techniques. Along the way, he explains methods of measuring performance. He provides examples of each forecasting technique with clean code in Python. Because his code is well documented, many will be able to follow and understand.I gladly recommend Data Science For Supply Chain Forecast to anyone interested in supply chain forecasting or looking for an intro to machine learning applications in supply chain. Five out five.
I have recently started in a new role as a demand forecasting analyst. Through my graduate studies I understood the theoretical aspect of forecast accuracy, however, as I am learning on the job i have found this book to be most useful for learning various practical applications, and also triggering  my thought process when I am working on a task.
Nicolas Vandeput made a great product with this easy to read, vibrant, and highly relevant book. There is no non-sense about this book. It is very well structured; providing you with easy to grasp concepts, the rationale for using one vs. the other, and simple math, as needed and necessary. The book offers easy-to-follow examples in Excel and Python (my preferred tool). The book also gives you access to download the same datasets discussed in the book so you can practice and follow each step of the way.  Even as a seasoned SC practitioner, I find this book helpful and practical.  Nicolas, thank you for a book well written.
This is a good book for learning the material, but the examples have errors in them.  There are many incorrect cell references in the text, which makes it very confusing.  For example, on page 79 it says "in cell D2 we type =B2/F2" and three lines later it says "in cell D2 we type=B3/F3-B2/F2".  Which is it?  I have found many examples like this, which makes it very hard to follow.
The models are very basic concept with linear/logistic regression.Will NOT recommend to go with the concept.Not Recommended for ML.
This book covers the basics of time series forecasting in plain English with some basic Python examples. It's an easy read. There is very little content about the supply chain domain despite the book's title. Practitioners with some experience in forecasting and machine learning will likely find little value in this book.
It is not data science. It is linear models at most
Muy buen libro, excelente para personas que esten buscando una referencia sobre Forecast
In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolasâ€™ book is a perfect solution for professionals who need to combine practical supply chain experience with the mathematical and technological tools that can help us predict the future more reliably.
I am a recently graduated Computer Engineer who is interested in supply chain planning. This book helped me to grasp a lot of important aspects of statistics as well as the applied side. Nicolas does a great job in taking the theoretical and giving exact applications. All the code you need to get started is available online and in the book.
This text book is the wrong edition of the textbook I was looking for. It does serve most of the purpose i need it for though.
Half way through this book and Iâ€™m finding it easy to understand almost everything the author is trying to explain. Itâ€™s a good book for anyone to learn and relearn data structures and algorithms.
The book is still in good condition and the content is intriguingly good with detailed explanation and challenging practices for me to practice in my own free time!
Good fundamental python book.
Great Book really helping me with my studies!
as described,
Extremely clear, and reader-friendly without sacrificing depth and breadth of topics. Covers classic/important cs problems in addition to helping consolidate Python syntax
This is a well written nice book to have. I found it easy to read ..wish if it could cover more subject
Could be better with clearer explanation.  I wish the exercises actually provided actual worked out solutions.
I like that this book offers a lot of code for readers, and this book is available online in entirety. However, some of the code has errors. As I understand it, the previous edition had even more errors, so I guess it is an improvement.
The books cover is damaged. Poor quality.
This book is excellent for people who have had little experience on algorithms and at the same time gives access to more in depth knowledge using Python..
Author jumps around a bit on fundamental concepts/examples, but fairly good book to learn programming in Python.
It is an excellent book.It is very easy to read and comprehensive.I like it, I recommend this book.
Good condition. Very good
This is an exceptional resource well worth your time that will make you an incredibly more useful coder. There is a free online interactive version of the book that's even more engaging and that's highly recommended. In the book, the authors never get too caught on the abstractions and formulas, showing practical examples and applications for every single concept introduced - a clear balance between practice and theory.
fast delivery of book whose is as described
I am a self taught Ruby programmer and have had a difficult time teaching myself algorithms.  The other textbooks I've tried lost me with formal math notation and difficult to read pseudocode.  This book is much more approachable and takes advantage of modern web features to teach the content.Some code examples can be dynamically modified and run in the browser, video solutions to 'self checks' are embedded in the chapters, and there are also some nice quizzes sprinkled in the chapters.If you don't have a formal computer science education and want to teach yourself algorithms, I think this is the best place to start, even if you don't know anything about Python.
Its free online and interactive -> [...]The material in the book is wonderful. But you don't need to purchase it.
I bought this book because I'm a Data Scientist that needs to be able to communicate fluently with software developers, even though I don't have a formal computer science background. This book did a really great job at not just going over the key data structures and algorithms I need to know, but also demonstrating how they are actually implemented in python with real code/ examples. I'm still working my way through all the practice problems, but I feel like I've already learned a lot by just by reading through it. This will probably be staying on my desk as a reference for years to come.
Data structures and algorithms should be one of your first stops when you reach a beginner understanding of a programming language. This is the book I wish I'd had several years ago when I started. I struggled with learning these concepts on my own, but this book knocks it out of the park. The authors take a very complex topic and start you simple and progressively introduce the complexities so that you are not overwhelmed. Don't get my wrong, the book will definitely challenge you, but it seems the authors really thought of a great way to introduce data structures and algorithms to a beginner. Well done.
Just finished reading first 100 pages. Very well written with illustrative examples (codes and flow diagrams). Excellent book for a beginner and also refreshing for someone with intermediate python programming experience. I would say this is an excellent book to get introduced with Python as well as computer science basics for those who were not fortunate to have a 'computer science' degree.
been enjoying going through these algorithms and data structures as warm ups in the morning before doing my course work.
Not as pedagogical as Cormen, however very decent balance of real code(in python) to explain fundamental algorithms/ data structures.  The topics chosen to illustrate algos also hard to find described better in other books.Some sections like Knight's Tour with hieuristics,Infix/Postfix and parse trees are wonderful.Sections on splay trees, string processing, image processing are great read.
I really enjoyed this book, wish there were more examples. Easy to understand if you're just starting out learning about data structures and algorithms
Well written, a great introduction to data structures and a decent introduction to PYTHON.  The exercises are great learning tools.  If you already know python you can skip that part.  I used this in a college "data structures" class several years ago and still find it useful.
I have an upcoming interview soon and I needed to freshen up my knowledge in some of the topics. This book is really nice to have in studying algorithms and data structure in python. Every chapter has a detailed explanation and few examples that is well explained per line as well.
Great book!Great problems, explains things in a very pragmatic and clear manor.Highly recommend.
the online material for this book is very helpful for junior Python developers.
Loads of super information approximately  Python for Data Science. I purchased it and that I discern each person's need for it. I actually care approximately the effects. to this point, this effective guide to  Python for Data Science has been first-rate help. it was shown within the outcomes I encountered
This is an astounding reference book for individuals working with data science. Keep in mind, 80% of the exertion in AI, data investigation or data science as a rule is tied in with handling data and getting data . This book is for that reason and I believe it's the best book out there about data preparing, investigation and representation utilizing python. On the off chance that you search for bad-to-the-bone AI, go for different books. Strongly suggested!
This book does provide an understanding of fundamental concepts approximately Python for Information Science.This is additionally a speedy way to memorize industry recognized apparatuses and scientific concepts that are really being utilized by information scientist.This book is an outright need have book for any confident data researcher.Here in this given data is very point by point clarification and code to assist to induce code running.
This guide presents data on one of a kind elements of the language and helped me research extra approximately different systems and capabilities in Python. I learned special methods to conform to my programming style to conquer a few troubles.
It's an archetype handbook for me. at this point and in attendance it is trying for organizations to perceive how they container consume in a row science to rally round cultivate themselves to finish you may anticipate this is simply a group of tallness, that separate a pair of organizations give birth to had the decision to make sure success with it. I'm intrigued with this manual.
I liked this book better because it broke through some other books' lectures and abstracts and dove into the kind of data and scenarios that I am more likely to actually encounter in my job, rather than just memorize them. Plus I didn't have to fix or workaround outdated or outversioned python code as I have had to do with some online teaching forums.
This book the aggregate of the information amazingly straightforward to examine. This book I will learn Python For Information Science adequately.This book is astounding and truly highlights simple down to earth ways to utilize our understanding of how our brains handle data, and how and where we center to draw an gathering of people to specific data.
Adore this book. It contains the dynamic presentation that I was looking for and I'm caught within the dialect presently. Andrew Park portrayed the python clarifying how reasonable this dialect is for PC programming and how the components and setting of python dialect energizes a developer to comprehend the complete coding framework.
It's an ideal guide for me. Here and there it is difficult for organizations to perceive how they can utilize information science to help develop themselves to stop you may expect this is only a lot of tallness, that lone a couple of organizations have had the option to see accomplishment with it. I'm intrigued with this manual.
I have read most of the book. Python allows several programming paradigms including object-oriented programming. The oop is a way of structuring the code that makes it especially effective by organizing and reusing code, although its abstract nature makes it not very intuitive when it starts. Good to have one for reading
If youâ€™re interested in learning Python programming, then I would like to suggest your listening this book. I got it last week and within a week Iâ€™ve learned Python Machine programming basics.
The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.
This book was thoroughly amazing; I picked up the Kindle structure and had the decision to hear it out normally during my work drive.
I would highly be recommended to read this book, everyone. This book gave me a lot of information.
Everything written in this book bodes well, unquestionably a decent read! I prescribe this book and would peruse more Books from this writer.  Many thanks to the author for giving us such a beautiful book.
This book is basically meant for MBA students, so they can feel informed about data science. You'll learn the difference between classification vs regression and supervised vs unsupervised learning. But don't think this book will enable you to change careers.
This book reads like it's a compilation of a teenager's blog posts on Data Science. There is nothing of substance in this book. Don't waste your time here and go find a real book about data science.
This conduct presents facts on one of a manner basics of the language and helped me investigation further approximately unusual systems and capabilities in Python. I erudite out of the ordinary methods to conform to my brainwashing panache to overthrow a little troubles.
I am to some degree interested to memorize Python and this book could be a extraordinary asset to begin with. It's exceptionally well-written and simple to get it. I will re-read this book for hands-on set-up and hone when I am prepared. I suggest this book for all..
This book empowers me to construct up my ability. On account of the maker. I genuinely enchanted in this book. I had exceptionally small association with python going before scrutinizing the book any way I had the choice to lift it up rapidly.
I am to some degree interested to memorize Python and this book may be a extraordinary asset to begin with. It's exceptionally well-written and simple to get it. I will re-read this book for hands-on set-up and hone when I am ready.
This books encourages me to see how to utilize Python. On the off chance that you are a learner to Data Science, at that point read this book first. This will give you a strong establishment in information science utilizing python.
This book is phenomenal! The Substance of the book is exceptionally good.This book is basically simple and informative.I truly appreciate this book exceptionally much.Very valuable information.I exceedingly suggested this book.
This book is fabulous! The Content of the book is very good.This book is basically simple and informative.I truly appreciate this book much.Very helpful information.I energetically suggested this book.
This book is definitely for beginners as the concepts are step-by-step using examples, exercises and mini-projects. However, the author does well-explaining thoughts, and it is very in-depth.
The perfect book for the beginner of Python data science. everything is easy to understand. Able to develop your python scripting skills from beginner to pro. Overall it's a great book!
If you are looking for a career in data science and something intermediate to advanced to enhance your deep learning and data science aptitude, this is an excellent book to go with.
Good book with practical tips. Very useful in daily data wrangling, munging tasks using pandas numpy. Good coverage on visualizations. This may make the learning path easier.
This book is great because it explains concepts well and uses real-world examples. However, the only machine learning free part was the first chapter.
This is one of the best Python books I've used. All of the examples worked which is rare in programming books. Great book. Well written & informative.
I preferred this book and I am happy to include it to my library. I would suggest this direct for those who require such sort of data. It is worth.
Well-structured book approximately nuts and bolts of information taking care of in python. It'll not allow you all answers but give strong start.
This book has included various surprising plans and besides pleasing for us all. the data channel must be managed by control rules.
The book is very well written and author did a good job explaining every line of codes and concepts. Worth every penny!
I would recommend this book to someone who would like learn about machine learning, not data analysis or visualization.
It's a perfect guide for me. Sometimes it is hard for companies to see how they can use data science to help improve themselves to stop you may assume that this is just a bunch of height, that only a few companies have been able to see success with it. I'm impressed with this guidebook.
Great python book . step by step direction. I exceedingly suggest this book to everyone.
It is good for beginners.
Loads of super in a row approximately Python for numbers Science. I purchased it and that I see apiece people must for it. I in fact consideration approximately the effects. to this point, this of use lead to Python for information Science has been first-rate help. it was publicized contained by the outcomes I encountered
This book is elegantly composed and simple to follow. It's spared me from going through hours looking through the web to get to know the standard libraries. presently the book has a place with my short rundown of work area reference books. Suggested.
This book is fantastic! The Content of the book is very good.This book is simply easy and informative.I really enjoy this book very much.Very useful information.I highly recommended this book.
Ulrika JÃ¤gare has written an insightful book which covers a very broad range of topics answering key questions like â€“ Why is it important to transform to a Data-driven organization and how to get there? What is Data as an asset and Data as a Business? What role does a Chief Data Officer (CDO) play? How to find the right design for a Data-driven organization? What are the pitfalls to avoid when transforming to a Data driven organization? What is the difference between software development and data science? Why is data model management important? What are the essential technologies in a modern data architecture? What is Data Ethics and why is it important?In addition to the effective diagrams illustrating key topics across the various chapters, I especially appreciate the abundant tips which provide a practical angle to the topic at hand. These together with a multitude of reminders, enable the content of the book to be delivered in an effective and clear manner.If you are working on or interviewing for a role of a data scientist, data engineer, data analyst or a leadership role involving data, this is a must read book!
Many decision makers struggle with data science and machine learning because ... it is complicated. Ulrika does a great job of guiding the decision makers to make the right decisions. The book is very well organized with 5 parts that take the reader through essentials of data to business models and data monetization, For example, Building a Successful Data Science Organization has four chapters which touch upon all important aspects of this important but often unaddressed issue.The book is very well-written and easy to read with lots of thoughtful illustration and summary tables. Its in my ML essential library and I highly recommend it for anyone working on data science and machine learning.
Every company wants to hire data scientists. Fewer companies have a strategy in place to maximize the business value of data science and guidance to how to lead and manage data science teams and projects. This book gives valuable, excellent insights into all of the above challenges â€” and more. Ulrika is an experienced data science leader in one of the world's most innovative companies. This book is an excellent way to benefit from her year's of experience and leadership.
I rate Data Science Strategy For Dummies a four because it took a while to get interesting to me, maybe that was because I read Data Science For Dummies first, which was more technical. However, it's worth the read and is required reading for my team.
Data Science Strategy for Dummies (DSSFD) is an absolutely brilliant data strategy book. Ulrika spent over 15 years doing data strategy for Ericsson before she put pen to paper to write this book. Itâ€™s truly a gem for people whoâ€™d prefer to learn from her hard-earned battle scars (instead of trying and FAILING over and over on their own).(Full disclosure: I did not purchase this book. It was given to me by a friend)This book is tech-heavy. It details the fundamental data competencies and understandings you should have before attempting to put together a data strategy for your organization. Itâ€™s not easy reading. IT IS POWERFUL READING.Iâ€™ve read several data strategy books, so let me compare:Data Strategy by Bernard Marr - This book is basically a collection of use cases that are presented in a logical framework. In comparison, DSSFD has very little in terms of use cases and instead focuses on the tech / data fundamentals you need to know to actually be successful with your data project.The Chief Data Officer Playbook - This book details some data strategy fundamentals (which I like), but it tends to focus a lot on telling readers how to champion data projects across an organization. DSSFD definitely covers these details, because they are vital to project success. That said, DSSFD is again much more thorough on addressing the tech / data details upon which one should be solid before attempting to design and lead an enterprise-wide data strategy.
This book has been a life saver! It brings simple clarity to complex topics. To someone who is trying to implement data science without any programming experience, many topics can be so confusing and it is hard to understand â€œthe right bookâ€ this book has been an amazing handbook I always carry around with me. GREAT BOOK!!!
In comparison to the (excellent) 3rd edition, the major difference are  two new chapters on probabilistic models and on deep learning.It is certainly good to have these more recent topics covered. However, the writing style of the two chapters is totally different: While the other chapters contain lots of examples and are didactically very well developed, the new chapters just present the theory in a take-it-or-leave-it style - lots of formulas, without any examples or any attempt to make the material more accessible for the average reader. This is really a pity.So I suggest to buy the third edition instead, and get the material for the new topics from elsewhere.
I used this book as a reference for a class I taught in statistical learning.  The new edition has a few new chapters that are a bit more technical than the rest of the book.  I am not sure if this book (or any other book out there) can by itself be used to learn about data mining.  However, I still think this is a great book.  The chapter on deep learning (a new chapter) is exceptionally clear and well written.
this was returned
I'm impressed with the transaction. Keep it up!
Good book to have in your shelf
The best way to have a solid knowledge in this field
I have a math/stat background but found this book totally unapproachable. It seems to try to appeal to multiple audiences, but from my (frustrating) reading, appeals to none of them. I ended up tossing it.To wit, the authors and I have a different idea of what "Practical" means. I was hoping to get this book as a guide to actually DOING something with different techniques. No idea who the target audience actually is...
Great book on how to use data mining for war
Review applies to the kindle edition.  While the information in the book is good, the formatting of the kindle addition is terrible.  No chapter indexes, so page numbers, percentage, no navigation.  If the Kindle edition is updated, I'll update my review.
This was a gift for someone else.  The recipient is enjoying reading and learning from this edition.  Thank you for the fast delivery!
I've read and reviewed the 1st, 2nd and now the 4th edition. The authors are genuine experts, at the front of their fields, and by adding new contributors have been able to both update existing topics as well as add authoritative treatments of new ones. I recommend this text to anyone seeking a serious introduction to data mining. The emphasis is practical rather than theoretical, but there are pointers to the theoretical literature for those wanting them. The practical emphasis serves those wanting such, and provides motivation and context for the approach. For those with the necessary mathematical, statistical and computing background there are certainly a plethora of more advanced treatments, but Witten et.al. may well be the best available introduction to the subject for almost everyone.