Machine learning has become an integral
part of many commercial applications and research projects, but this
field is not exclusive to large companies with extensive research teams.
If you use Python, even as a beginner, this book will teach you
practical ways to build your own machine learning solutions.
With all
the data available today, machine learning applications are limited only
by your imagination.
You’ll learn the steps necessary to create a
successful machine-learning application with Python and the
scikit-learn library.
Authors Andreas Müller and Sarah Guido focus on
the practical aspects of using machine learning algorithms, rather than
the math behind them. Familiarity with the NumPy and matplotlib
libraries will help you get even more from this book.
With this book, you’ll learn:
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills
[Book Description Source: www.amazon.com ]
Ratings
Goodreads Rating - 4.55 out of 5 ( 11 Ratings; 0 Reviews - As on October 30 2017)
My Rating: 4 out of 5
My Comments:
A very good introduction to machine learning concepts.
Systematic organization of the topics, and ample examples provided makes it a worthwhile read.
Several important algorithms have been discussed along with their pros and cons with minimum use of advanced mathematics.
Much much better book to start with Machine Learning as compared to the "Machine Learning for Dummies" book which was pedagogically horrible.
.
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Machine learning can be a mind-boggling concept for the masses, but
those who are in the trenches of computer programming know just how
invaluable it is.
Without machine learning, fraud detection, web search
results, real-time ads on web pages, credit scoring, automation, and
email spam filtering wouldn't be possible, and this is only showcasing
just a few of its capabilities.
Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.
Covering
the entry-level topics needed to get you familiar with the basic
concepts of machine learning, this guide quickly helps you make sense of
the programming languages and tools you need to turn machine
learning-based tasks into a reality.
Whether you're maddened by the math
behind machine learning, apprehensive about AI, perplexed by
preprocessing data—or anything in between—this guide makes it easier to
understand and implement machine learning seamlessly.
- Grasp how day-to-day activities are powered by machine learning
- Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis
- Learn to code in R using R Studio
- Find out how to code in Python using Anaconda
Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
[Book Description Source: www.amazon.com ]
Ratings
Goodreads Rating - 3.59 out of 5 ( 32 Ratings; 0 Reviews - As on September 30 2017)
My Rating: 2 out of 5
My Comments:
At the best this is a OK sort of reference book for people who already have a fair exposure to Machine Learning.
Definitely not for dummies as the title of the book proclaims.
The authors may be experts in this field but certainly don't know the ABCs of how to teach absolute beginners.
The content is badly organized and jargons are thrown around with gay abandon all over the place thus overwhelming the newbies.
Most of the readers who start reading this book to learn Machine Learning may give up soon and switch over to better books available in the market.
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Discover how data science can help you gain in-depth insight into your business – the easy way!
Jobs
in data science abound, but few people have the data science skills
needed to fill these increasingly important roles in organizations.
Data Science For Dummies
is the perfect starting point for IT professionals and students
interested in making sense of their organization’s massive data sets and
applying their findings to real-world business scenarios.
From
uncovering rich data sources to managing large amounts of data within
hardware and software limitations, ensuring consistency in reporting,
merging various data sources, and beyond, you’ll develop the know-how
you need to effectively interpret data and tell a story that can be
understood by anyone in your organization.
- Provides a
background in data science fundamentals before moving on to working with
relational databases and unstructured data and preparing your data for
analysis
- Details different data visualization techniques that can be used to showcase and summarize your data
- Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques
- Includes coverage of big data processing tools like MapReduce and Hadoop
It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.
[Book Description Source: www.amazon.com ]
Ratings
Goodreads Rating - 2.22 out of 5 (9 Ratings; 1 Review)
My Rating: 3 out of 5. Provides a good overview of lots of topics, but definitely not written in a manner so that dummies can understand. It is more like a refresher for experts.
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Good software development results in simple code.
Unfortunately, much of
the code existing in the world today is far too complex.
This concise
guide helps you understand the fundamentals of good software development
through universal laws--principles you can apply to any programming
language or project from here to eternity.
Whether you're a
junior programmer, senior software engineer, or non-technical manager,
you'll learn how to create a sound plan for your software project, and
make better decisions about the pattern and structure of your system.
- Learn what differentiates great programmers from poor programmers
- Understand the ultimate purpose of software and the goals of good software design
- Determine the value of your decisions now and in the future
- Examine real-world examples that demonstrate how a system changes over time
- Learn to allow for the most change in the environment with the least change in the software
- Make easier changes in the future by keeping your code simpler now
- Understand why to write tests and what tests to write.
[Book Description Source: Amazon.com]
Goodreads Rating - 3.47 out of 5 (169 Ratings, 40 Reviews)
My Rating: 3 out of 5.
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Synopsis
- Describes cutting edge developments in the power and possibilities of the Web
- Explains how Google and other leading companies have developed ever smarter searching and advert placement
- Shows how 'Web intelligence' is emerging
from increasingly smart algorithms that can sift, select, aggregate, and
weigh up relevant information
- Explains the principles of logic and computer science behind these apparently intelligent procedures
- Considers possible similarities with how the human brain handles data
- Looks at new applications such as self-driving cars
As
we use the Web for social networking, shopping, and news, we leave a
personal trail.
These days, linger over a Web page selling lamps, and
they will turn up at the advertising margins as you move around the
Internet, reminding you, tempting you to make that purchase. Search
engines such as Google can now look deep into the data on the Web to
pull out instances of the words you are looking for.
And there are pages
that collect and assess information to give you a snapshot of changing
political opinion.
These are just basic examples of the growth of "Web
intelligence", as increasingly sophisticated algorithms operate on the
vast and growing amount of data on the
Web, sifting, selecting, comparing, aggregating, correcting; following
simple but powerful rules to decide what matters.
While original
optimism for Artificial Intelligence declined, this new kind of machine
intelligence is emerging as the Web grows ever larger and more
interconnected.
Gautam Shroff takes us on a journey through the
computer science of search, natural language, text mining, machine
learning, swarm computing, and semantic reasoning, from Watson to
self-driving cars.
This machine intelligence may even mimic at a basic
level what happens in the brain.
Readership: Popular science and mathematics readers interested in computer science and artificial intelligence, as well as
students of these courses.
[Source: Oxford University Press website.]
My Comments:
I completed an online course "Web Intelligence and Big Data" taught by the author a few months back. This book was released towards the ending of the course and I bought a Kindle edition to understand the topics better.
Gautam Shroff has taken a very logical and systematic approach to explain the fascinating area of Web Intelligence.
While the topics in this book are really interesting, the verbose explanations given by the author on many of the concepts were quite confusing. More illustrations would have helped. The book has just about half-a-dozen pictures, which for a content of 300 odd pages on technical topics is quite less.
It will take another reading of the book for me to fully grasp the explanations.
But overall this book is quite a good introduction to Web Intelligence, though not a very light read.
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Goodreads Rating - 4.2 out of 5 ( 5 Ratings)
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