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MACHINE LEARNING WITH PYTHON: An introduction to Data Science with useful concepts and examples, step by step, learning to use Python
Do you Know exactly M.L why is it so valuable in data business ?
Are you thinking of learning but are you afraid it’s not enough ?
This book teaches you, thanks to Python, the ways to do it !
Understand the various categories of machine learning algorithms.
Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more
Latest Python open source libraries in ML ML techniques using real-world data The ML Classifiers Using Scikit-Learn Implementing a Multilayer Artificial Neural Network from Scratch The Mechanics of TensorFlow ML Model into a Web Application The future of ML
Numpy Pandas
Matplotlib
Are you thinking of learning but are you afraid it’s not enough ?
This book teaches you, thanks to Python, the ways to do it !
Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions – this is important in predicting the world around us.
Today, ML algorithms accomplish tasks that until recently only expert humans could perform and, as machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities.
Programmers who know close to nothing about this technology, now, can use simple, efficient tools to implement programs capable of learning from data.
Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields.
Inside "Machine Learning with Python" you’ll learn:
Understand the various categories of machine learning algorithms.
Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more
Latest Python open source libraries in ML ML techniques using real-world data The ML Classifiers Using Scikit-Learn Implementing a Multilayer Artificial Neural Network from Scratch The Mechanics of TensorFlow ML Model into a Web Application The future of ML
You are required to have installed the following on your computer:
Numpy Pandas
Matplotlib
Throughout the recent years, artificial intelligence and machine learning have made some enormous, significant strides in terms of universal, global applicability. You’ll discover the steps required to develop a successful machine-learning application using Python.
This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques.
393 pages, Kindle Edition
Published July 30, 2019
About the author
William Gray
172 books4 followersLibrarian Note:
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There is more than one author in the Goodreads database with this name.
This profile may contain books from multiple authors of this name.
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Displaying 1 - 1 of 1 review
July 27, 2020
Probably one of the worst books I have picked up.
I read lots of textbooks and course literature in my profession, some good some bad, but this one is beyond poor! It is not as advertised on a level for an absolute beginner, nor is advanced enough for non-beginners.
The typesetting is really poor, the spacing is inconsistent, the types of bullets use changes between bullet-lists . In a span of nine pages are six different types (regular bullets, arrows, second type of arrows, check-marks, crosses and squares) used in six different lists. The code examples might have been easier to read had the publisher used a different font than the regular text. I do not recommend this book, save the money and buy something better.
I read lots of textbooks and course literature in my profession, some good some bad, but this one is beyond poor! It is not as advertised on a level for an absolute beginner, nor is advanced enough for non-beginners.
The typesetting is really poor, the spacing is inconsistent, the types of bullets use changes between bullet-lists . In a span of nine pages are six different types (regular bullets, arrows, second type of arrows, check-marks, crosses and squares) used in six different lists. The code examples might have been easier to read had the publisher used a different font than the regular text. I do not recommend this book, save the money and buy something better.
Displaying 1 - 1 of 1 review


