Brandon Railey

Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?

If you responded yes to any of the above questions, you have come to the right place. 

Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. 

Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it?

Apart from this, you will also learn more about:

- The Different Types Of Learning Algorithm That You Can Expect To Encounter
- The Numerous Applications Of Machine Learning And Deep Learning
- The Best Practices For Picking Up Neural Networks
- What Are The Best Languages And Libraries To Work With
- The Various Problems That You Can Solve With Machine Learning Algorithms
- And much more…

Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network?

Python Machine Learning: A Practical Beginner's Guide to Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras

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