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This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here.
With the prevalence of computer science constantly rising, knowing at least the basics of machine learning systems is extremely valuable in business. Today we will be discussing 8 of the best machine learning books, from beginner to expert level, along with the topics covered in each, where you can get a copy, and the next steps you can take after reading these books. Let’s get started.
Beginner books
1. Machine Learning for Absolute Beginners: A Plain English Introduction
Topics covered:
Downloading free datasets
Tools and machine learning libraries you need
Data scrubbing techniques (includes one-hot encoding, binning and dealing with missing data)
Preparing data for analysis (includes k-fold Validation)
Regression analysis to create trend lines
Clustering (includes k-means and k-nearest Neighbors)
The basics of Neural Networks
Bias/Variance to improve your machine learning model
Decision Trees to decode classification
Building your first ML model to predict house values using Python
Price: $14.80 Author: Oliver Theobald Where to buy: Amazon
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk (Co-founder/CEO of Tesla and SpaceX, Co-chair of OpenAI)
Price: $70.00 Author: Ian Goodfellow, Yoshua Bengio, & Aaron Courville Where to buy: Amazon
Next steps
These books teach the ins-and-outs of ML, but that’s only the first step. If you’re interested in working in machine learning, your next steps would be to practice engineering ML. If you’re part of a business that uses ML, and your organization needs a way of implementing machine learning models efficiently at scale, then that’s where Algorithmia steps in. We created a serverless microservices architecture that allows enterprises to easily deploy and manage machine learning models at scale.
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