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The world of technology is growing all the time. It seems like a new technology is coming out all of the time, and it seems like it is outpacing what most traditional coding languages are able to do. While there is a lot that you are able to with traditional forms of coding, it isn't able to meet all of your needs. What if we were able to make a program that was able to learn on its own? What if we could put in a bit of information, and the program were able to do what it needed to, and take control. This is where the beauty of machine learning is going to come into play! This guidebook is going to take some time to look at machine learning, and how you are able to work with the Python language in order to make it work and to help you create some of the best programs out there! Imagine all that you can do when you bring in machine learning and can create programs and more that can think for and learn on their own! Some of the topics that we are going to explore with Python machine learning inside this guidebook include: The different types of machine learning that you are able to work with. The difference between machine learning and deep learning. How to set up and use the Scikit-learn library from Python. How to set up and use the TensorFlow library. The K-Nearest Neighbors and the K-Means clustering algorithms. How to use support vector machines with machine learning. Working with neural networks and recurrent neural networks. How decisions trees can help you make smarter decisions, and turning these decision trees into random forests. Working with linear classifiers when you are in machine learning....and much, much more! There are so many things that we are able to work with when it comes to machine learning, and the field is going to grow in leap and bounds through th
This guidebook explores the practical application of machine learning using the Python programming language. It aims to demystify the field of machine learning, explaining its core concepts and demonstrating how to implement them with Python libraries. The book covers various machine learning types, differentiates between machine learning and deep learning, and provides hands-on guidance for using essential tools like Scikit-learn and TensorFlow.
The book is presented as a practical guide for individuals looking to implement machine learning with Python. It focuses on hands-on application and covers a range of algorithms and libraries relevant to the field. The content suggests an approach that bridges theoretical concepts with practical coding examples, aiming to equip readers with the skills to build intelligent programs. The detailed list of topics indicates a comprehensive overview for those entering or advancing in machine learning.
Page Count:
182
Publication Date:
2020-12-04
Publisher:
Digital Marketing Revolution Limited
ISBN-10:
1914015770
ISBN-13:
9781914015779
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