Prerequisites
This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.
Also, if you care about what’s under the hood, you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).
More about this book
Machine Learning in Your Projects
So, naturally you are excited about Machine Learning and would love to join the party! Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or teach it to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and you could likely unearth some hidden gems if you just knew where to look. With Machine Learning, you could accomplish the following:
- Segment customers and find the best marketing strategy for each group
- Recommend products for each client based on what similar clients bought
- Detect which transactions are likely to be fraudulent
- Forecast next year’s revenue
- And more
Objective and Approach
This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data. We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions.
Rather than implementing our own toy versions of each algorithm, we will be using production-ready Python frameworks:
- Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning.
- TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train & run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU servers. TensorFlow was created at Google and supports many of its large-scale applications. It's been open source since Nov. 2015, with version 2.0 releasing Oct 2019.
- Keras is a high-level Deep Learning API that makes it very simple to train and run neural networks. It can run on top of either TensorFlow, Theano, or Microsoft Cognitive Toolkit (formerly known as CNTK). TensorFlow comes with its own implementation of this API, called tf.keras, which provides support for some advanced TensorFlow features (e.g., the ability to efficiently load data).
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| Machine Learning Pocket Reference | Hands-On Unsupervised Learning Using Python | Practical Automated Machine Learning on Azure | Generative Deep Learning | |
| Additional Machine Learning From O'Reilly Media | Working with Structured Data in Python | How to Build Applied Machine Learning Solutions from Unlabeled Data | Using Azure Machine Learning to Quickly Build AI Solutions | Teaching Machines to Paint, Write, Compose, and Play |
