What You Need to Know About Data Mining and Data-Analytic Thinking
Written by renowned data
science experts Foster Provost and Tom Fawcett, Data Science for
Business introduces the fundamental principles of data science, and
walks you through the "data-analytic thinking" necessary for extracting
useful knowledge and business value from the data you collect.
This
guide also helps you understand the many data-mining techniques in use
today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business
provides examples of real-world business problems to illustrate these
principles.
You’ll not only learn how to improve communication between
business stakeholders and data scientists, but also how participate
intelligently in your company’s data science projects.
You’ll also
discover how to think data-analytically, and fully appreciate how data
science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
[Book Description Source: www.amazon.com ]
Ratings
Goodreads Rating - 4.17 out of 5 ( 932 Ratings ,67 Reviews - As on Dec 14 2017)
My Rating: 4 out of 5
My Comments:
This book is an excellent Executive Guide on Data Science. Written in a fairly non-technical manner focusing more on business perspective rather than deep technicalities. Serves as a very good introduction to Data Science..
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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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