Almost all top universities around the world offer machine learning programs given the importance of the subject, its popularity among students, and the diversity of its applications. The subject itself may, however, seem complex for beginners. Therefore, a book covering the subject in the “for dummies” series is most welcome.

Following the tradition of the series, the writing style is very clear, and no exotic prerequisites are needed in order to understand the content.

The book is divided into 23 chapters grouped into six parts.

Part 1 (chapters 1 to 3) lays the foundations of machine learning, including its history, main applications, and relationships with artificial intelligence and statistics. Part 2 (chapters 4 to 8) is dedicated to the installation and coding of R and Python, the two main languages that will be used to implement machine learning applications. Part 3 (chapters 9 to 12) covers basic mathematics that every machine learning developer needs to master in order to be able to implement real applications.

By Part 4 (chapters 13 to 18), the reader should be armed with the right tools to experiment with pre-processing data, linear models, neural networks, and the main processes and mathematical models widely used in machine learning applications. Part 5 (chapters 19 to 21) discusses in depth three real applications that use machine learning: image classification, sentiment analysis on the web, and recommendation systems. Part 6 (chapters 22 and 23) extends the previously covered material with machine learning packages a developer needs to master if she wants to develop more sophisticated applications. This includes Cloudera Oryx, RandomForest, and SciPy. This part also discusses ten ways to improve machine learning models, including cross-validation, averaging, and stacking models.

One of the strongest points of the book is that it explores machine learning using the best tools available today, namely the Python and R languages. It also covers the main mathematical concepts needed to implement some basic machine learning applications. This strategy allows the reader to quickly gain confidence, providing him with the motivation to carry on with more complex concepts.

Machine learning for dummies is up to date with the latest information on machine learning and development tools. It is also easy to follow. The book provides an excellent reference on the subject and therefore is highly recommended to beginners.

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