What do you think?
Rate this book
Machine Learning from Scratch: Practical Guide with Python
***** Buy now (Will soon return to $38.95 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Machine Learning From Scratch by using Python? The overall aim of this book is to give you an application of machine learning techniques with python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what might happen in the future. This book is a practical guide through the basic principles of machine learning, and how to get started with machine learning using Python based on libraries that make machine learning easy to get started with.
Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images, which detail all-important Machine learning concepts and their applications.
This Is a Practical Guide Book This book will help you explore exactly the most important machine learning techniques by using python and real data. It is a step-by-step book. You will build our Machine Learning Models by using Python
Target Users The book designed for a variety of target audiences. The most suitable users would include:
Beginners who want to approach data science, but are too afraid of complex math to start
Newbies in computer science techniques and machine learning
Professionals in data science and social sciences
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on data science
What's Inside This Great Book?
Introduction
Using Python for Machine Learning
Steps to Solving Machine Learning Problems
A Machine Learning Example: Predicting Housing Prices
Here's Where Real Machine Learning Starts
What If Regression Doesn't Apply?
How to Improve Your Model's Performance
How to Improve Your Model's Performance
Neural Networks & Deep Learning
The Future of Machine Learning
Glossary on Important Machine Learning Terms
Sources & References
Bonus Chapter: Anaconda Setup & Python Crash Course
Frequently Asked Questions
Q: Is this book for me and do I need programming experience? A: f you want to smash Data Science from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.
Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.
Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and further learning will be required beyond this book to master all aspects of data science.
Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at customer_service@datasciences-book.com.
Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images, which detail all-important Machine learning concepts and their applications.
This Is a Practical Guide Book This book will help you explore exactly the most important machine learning techniques by using python and real data. It is a step-by-step book. You will build our Machine Learning Models by using Python
Target Users The book designed for a variety of target audiences. The most suitable users would include:
Beginners who want to approach data science, but are too afraid of complex math to start
Newbies in computer science techniques and machine learning
Professionals in data science and social sciences
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on data science
What's Inside This Great Book?
Introduction
Using Python for Machine Learning
Steps to Solving Machine Learning Problems
A Machine Learning Example: Predicting Housing Prices
Here's Where Real Machine Learning Starts
What If Regression Doesn't Apply?
How to Improve Your Model's Performance
How to Improve Your Model's Performance
Neural Networks & Deep Learning
The Future of Machine Learning
Glossary on Important Machine Learning Terms
Sources & References
Bonus Chapter: Anaconda Setup & Python Crash Course
Frequently Asked Questions
Q: Is this book for me and do I need programming experience? A: f you want to smash Data Science from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.
Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.
Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and further learning will be required beyond this book to master all aspects of data science.
Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at customer_service@datasciences-book.com.
122 pages, Paperback
Published March 6, 2017
2 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
February 11, 2019
A good start to get the basics of machine learning through examples.
Displaying 1 of 1 review