Machine Learning kompakt: Alles, was Sie wissen müssen

Machine Learning Kompakt Cover und Deep-Learning-Kapitel
Machine Learning kompakt und Blick in das Kapitel “Neuronale Netze und Deep Learning”

Nachdem ich bereits Erfahrung als Buchautor (hier und hier) gesammelt habe, hatte ich kürzlich die Gelegenheit als Technical Reviewer ein sehr spannendes Buchprojekt zu unterstützen. Das Buch Machine Learning kompakt: Alles, was Sie wissen müssen, geschrieben von Andriy Burkov, fand ich dabei dermaßen interessant, dass ich es gerne im Folgenden kurz vorstellen werde:

Machine Learning kompakt von Andriy Burkov ist ein hervorragend geschriebenes Buch und ein Muss für jeden, der sich für Machine Learning interessiert.

Andriy Burkov gelang ein ausgewogenes Verhältnis zwischen der Mathematik, intuitiven Darstellungen und verständlichen Erklärungen zu finden. Dieses Buch wird Neulingen auf dem Gebiet als gründliche Einführung zu Machine Learning zugutekommen. Darüber hinaus dient das Buch Entwicklern als perfekte Ergänzung zu Code-intensiver Literatur, da hier die zugrunde liegenden Konzepte beleuchtet werden.

Microsoft Azure Machine Learning Studio
Microsoft Azure Machine Learning Studio

Machine Learning kompakt eignet sich außerdem als Lehrbuch für einen allgemeinen Kurs zu Machine Learning. Ich wünschte, ein solches Buch gäbe es, als ich studiert habe!

Protip: viele der im Buch vorgestellten Machine-Learning-Algorithmen können Sie einfach und bequem in Microsoft Azure Machine Learning Studio selbst ausprobieren:

How to Master Your Data Journey

Covers of Info We Trust, Visual Analytics with Tableau, The Big Book of Dashboards
From ideas to dashboards: The Data Journey Canon

Last year I started lecturing a Data Analytics course at university (as part of an MBA program). In the meanwhile, I was refining the list of books that I highly recommend to read. Three of these books form the Data Journey!

What is the Data Journey?

The Data Journey is a human-focused approach to understand the evolution of data storytelling, the power of visual analytics, and the impact of data from real-world business scenarios.

1. Info We Trust: How to Inspire the World with Data

We start our Data Journey with the book Info We Trust. This book examines all parts of the data storytelling lifecycle across disciplines. The use of marginalia and hand-drawn illustrations give you both simple lessons to take away, and insights into where to find out more. The book is full of magnificent references that inspire further reading.

2. Visual Analytics with Tableau

Now it’s time for hands-on. Visual Analytics with Tableau covers everything you need to get started with Tableau (students get Tableau for free!). The book guides you from the first steps of connecting to data, creating different types of charts, and adding calculation fields to more advanced features such as table calculations, forecasts, and clusters, as well as R, Python, and MATLAB integration for sophisticated statistical modeling.

3. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

Back to business (as this course is designed for an MBA program). We close our Data Journey canon with The Big Book of Dashboards. This is a comprehensive reference book with real-world solutions for business dashboards and detailed analysis of do’s and don’ts. The examples in this book are well-organized and categorized by industry and functional business areas.

Update 10 July 2019: Do you need more inspiration?

#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time

Because vizzing alone is only half the fun, you should not miss the #MakeoverMonday book. Eva Murray and Andy Kriebel are icons in the data visualization community (read the interview!) and they have curated the thousands of visualizations from the #MakeoverMonday project into a practical guide that will take your design and data communication skills to the next level!

Which books would you recommend for the #DataJourney?

Share your favorite data books in the blog post comments or reply to this tweet:

How China is winning in the Age of Artificial Intelligence

Alibaba Campus
Alibaba Campus

Currently, I’m on a 4-week China trip, visiting many cities. In Hangzhou, I met CEIBS peers who work for Alibaba. While the Alibaba campus is quite impressive, I got even more impressed by Alibaba’s leadership culture, which is encouraging its employees to innovate as intrapreneurs.

If you start your own project (a new mobile app, a patent, a scientific paper, etc.), you’re doing it in your own pace, you’re not being micro-managed and you’ll receive a bonus based on success. Intrapreneurship at Alibaba is just one of many examples where we (Europeans) can learn a lot from China!

Yue and me, Hangzhou West Lake

While traveling in China I was reading AI Superpowers: China Silicon Valley, and the New World Order by Kai-Fu Lee, a book that is a must-read to get an idea where China’s AI ambitions are heading to. What matters most for AI innovation these days, the author argues, is access to vast quantities of data—where China’s advantage is overwhelming.

A quite entertaining book focusing on the new mindset of China’s young generation is this one: Young China: How the Restless Generation Will Change Their Country and the World by Zak Dychtwald.

: Which other cities in China did I visit? Check out my Tableau Public viz:

Die 5 wichtigsten Bücher zu Data Science

Welche Bücher sollten Sie lesen um als Data Scientist erfolgreich zu sein? | Photo Credit: via Sebastian Sikora

Für den Einstieg und den Überblick zu Data Science:

Die universelle Programmiersprache Python eignet sich hervorragend zur Lösung von Data-Science-Fragestellungen:

Eine Einführung zu Statistical Learning und Machine Learning (classification, clustering, supervised, unsupervised, …) mit R:

Bei großen Datenmengen führt an Hadoop kein Weg vorbei:

Wie Data Science im Unternehmen Mehrwert schafft: