Python Machine Learning: Understand Python Libraries (Keras, NumPy, Scikit-lear, TensorFlow) for Implementing Machine Learning Models in Order

By Ethem Mining

Do you want to learn how to apply efficiently your Python knowledge to implement learning models? Do you want to understand which ones are the best libraries to use and why is Python considered the best language for machine learning? What do you need to learn to move from being a complete beginner to someone with advanced knowledge of machine learning?

Tech is slowly moving towards high-level automation, robotics, machine learning, artificial intelligence, big data and other high level computing concepts.

That's why self-driving cars, customized product recommendations, real time pricing, facial recognition, retargeting ads, geo-targeting, using bots for customer service and much more is a thing these days.

So if you ever want to leverage the full power of any of these advanced computing concepts, now is the right time to get in

So where do you even start?

Well, my recommendation is to start by learning machine learning, as that will effectively help you to understand the ins and outs of how to build intelligent systems.

The book will teach you:

The basics about machine learning, including what it is, how it developed, the place of big data in machine learning as well as how machine learning works

How machine learning works in 7 simple steps

How machine learning is applied in real world situations like health care, customer service, underwriting, real time pricing, self-driving cars, fraud detection, robotics, facial recognition, product recommendations, retargeting customers and much more

How supervised learning is a thing in machine learning, including the types of supervised learning, feature vectors, how to pick the learning algorithm and more

How to leverage the power of unsupervised machine learning, including what unsupervised learning means, how to use different approaches to clustering and, visualization

How you can use semi-supervised learning as well as reinforcement based learning, where both of them are used and more

The place of regression techniques in machine learning, including the different regression methods that you can use as well as how to use them well

How data is classified in machine learning, including the different methods of classifying data

How to unleash the full power of neural networks in machine learning while leveraging the power of different libraries like TensorFlow, Keras and more

Multiple ways to access computing power in machine learning

How to unleash the full power of data mining using different libraries like The Scikit-Learn

How to make the most use of NumPy Ndarray for high-level operations and in neural networks

And much more

Even if this is your first encounter with the machine learning and want to dip your feet into the world of high level computing concepts like machine learning, deep learning, artificial intelligence and more, this book will break everything using easy to follow language to help you to apply what you learn right away

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• Format: Paperback
• Language: English
• ISBN: 1671257901
• ISBN13: 9781671257900
• Release Date: December 2019
• Publisher: Independently Published
• Length: 246 Pages
• Weight: 0.99 lbs.
• Dimensions: 0.5" x 6.0" x 9.0"