Emmanuel Ameisen - Building Machine Learning Powered Applications: Going from Idea to Product

• If youd like to know ML well enough to write your own algorithms from scratch, I recommend Data Science from Scratch, by Joel Grus. If the theory of deep learning is what you are after, the textbook Deep Learning (MIT Press), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, is a comprehensive resource.

• If you are wondering how to train models efficiently and accurately on specific datasets, Kaggle and fast.ai are great places to look.

• If youd like to learn how to build scalable applications that need to process a lot of data, I recommend looking at Designing Data-Intensive Applications (OReilly), by Martin Kleppmann.

If you have coding experience and some basic ML knowledge and want to build MLdriven products, this book will guide you through the entire process from product idea to shipped prototype. If you already work as a data scientist or ML engineer, this book will add new techniques to your ML development tool. If you do not know how to code but collaborate with data scientists, this book can help you understand the process of ML, as long as you are willing to skip some of the in-depth code examples.

Lets start by diving deeper into the meaning of practical ML.

For the purpose of this introduction, think of ML as the process of leveraging patterns in data to automatically tune algorithms. This is a general definition, so you will not be surprised to hear that many applications, tools, and services are starting to integrate ML at the core of the way they function.

Some of these tasks are user-facing, such as search engines, recommendations on social platforms, translation services, or systems that automatically detect familiar faces in photographs, follow instructions from voice commands, or attempt to provide useful suggestions to finish a sentence in an email.

Some work in less visible ways, silently filtering spam emails and fraudulent accounts, serving ads, predicting future usage patterns to efficiently allocate resources, or experimenting with personalizing website experiences for each user.