Today, machine learning offers software professionals unparalleled opportunity for career growth. In Introducing Machine Learning, best-selling software development author, trainer, and consultant Dino Esposito offers a complete introduction to the field for programmers, architects, lead developers, and managers alike.
Esposito begins by illuminating what’s known about how humans and machines learn, introducing the most important classes of machine learning algorithms, and explaining what each of them can do. Esposito demystifies key concepts ranging from neural networks to supervised and unsupervised learning. Next, he explains each step needed to build a successful machine learning solution, from collecting and fine-tuning source data to building and testing your solution.
Then, building on these essentials, he guides you through constructing two complete solutions with ML.NET, Microsoft’s powerful open source and cross-platform machine learning framework. Step by step, you’ll create systems for performing sentiment analysis on social feeds, and analyzing traffic to predict accidents. By the time you’re finished, you’ll be ready to participate in data science projects and build working solutions of your own.
Dino Esposito: If I look back, I count more 20 books authored and 1000+ articles in a 25-year-long career. I’ve been writing the “Cutting Edge” column for MSDN Magazine month after month for 22 consecutive years. It is commonly recognized that such books and articles have helped the professional growth of thousands of .NET and ASP.NET developers and software architects worldwide.
After I escaped a dreadful COBOL project, in 1992 I started as a C developer, and since then, I have witnessed MFC and ATL, COM and DCOM, the debut of .NET, the rise and fall of Silverlight, and the ups and downs of various architectural patterns. In 1995 I led a team of five dreamers who actually deployed things that today we would call Google Photos and Shuttershock–desktop applications capable of dealing with photos stored in a virtual place that nobody had called the cloud yet. Since 2003 I have written Microsoft Press books about ASP.NET and also authored the bestseller Microsoft .NET: Architecting Applications for the Enterprise. I have a few successful Pluralsight courses on .NET architecture, ASP.NET MVC UI, and, recently, ML.NET. As architect of most of the backoffice applications that keep the professional tennis world tour running, I’ve been focusing on renewable energy, IoT, and artificial intelligence for the past two years as the corporate digital strategist at BaxEnergy.
You can get in touch with me through https://youbiquitous.net or twitter.com/despos, or you can connect to my LinkedIn network.
Francesco Esposito: I was 12 or so in the early days of the Windows Phone launch, and I absolutely wanted one of those devices in my hands. I could have asked Dad or Mom to buy it, but I didn’t know how they would react. As a normal teenager, I had exactly zero chance of having someone buy it for me. So, I found out I was quite good at making sense of programming languages and impressed some folks at Microsoft enough to have a device to test. A Windows Phone was only the beginning; then came my insane passion for iOS and, later, the shortcuts of C#.
The current part of my life began when I graduated from high school, one year earlier than expected. By the way, only 0.006 percent of students do that in Italy. I felt as powerful as a semi-god and enrolled in mathematics. I failed my first exams, and the shock put me at work day and night on ASP.NET as a self punishment. I founded my small software company, Youbiquitous, and began living on my own money. In 2017, my innate love for mathematics was resurrected and put me back on track with studies and led me to take the plunge in financial investments and machine learning.
This book, then, is the natural consequence of the end of my childhood. I wanted to give something back to my dad and help him make sense of the deep mathematics behind neural networks and algorithms. By the way, I have a dream: developing a supertheory of intelligence that would mathematically explore why the artificial intelligence of today works and where we can go further.
You can get in touch with me at https://youbiquitous.net.
Part I: Laying the Ground (of Machine Learning)
Part II: Operations (of Machine Learning)
Part III: Hands-on Stuff
Other versions by this Author
