Want to learn machine learning? Looking for data science tutorials and guides to help you master your data and produce actionable, game-changing insights?
Look no further than this list of machine learning eBooks from the Packt team….
Python Machine Learning is today one of the most popular machine learning titles on the market. And it’s not hard to see why – by bridging the gap between theory and practice, the author Sebastian Raschka provides you with an accessible way to actually start building machine learning algorithms and to understand how and why they work.
2. Getting Started with TensorFlow
Built by Google, TensorFlow has had a huge impact on the machine learning and deep learning fields. It’s an accessible yet powerful library that is redefining who is building algorithms and how they’re building them. This book does exactly what it says on the tin – it provides you with the tutorials and insight you need to get up and running with TensorFlow in no time.
3. Large Scale Machine Learning with Spark
Big Data feels like a trend that’s been forgotten over the last few years as AI and neural networks have taken centre stage in data science circles. But to think it was ever just a buzzword was, of course, foolish. In fact, by bringing together machine learning and big data you have the opportunity to leverage truly impressive insights, managing data at scale with intricate algorithms. That’s exactly what this book does – taking the latest features of Spark 2.0, it shows you how to apply a wide range of machine learning algorithms to data problems.
4. Mastering Java Machine Learning
R and Python are the two languages that are typically fighting for top spot when it comes to data science. But other languages are starting to increase their share in the data science market – an indication, perhaps, of the wider growth of data science and machine learning. This book is in precisely that mould – taking one of the most popular languages in programming, it shows you how to apply the language to a huge variety of machine learning problems. Perfect for the curious machine learning engineer that wants to try out new languages, or even Java programmers that need to build machine learning systems, this book is a comprehensive, modern machine learning guide.
