The series Topics in Current Chemistry Collections presents critical reviews from the journal Topics in Current Chemistry organized in topical volumes. The scope of coverage is all areas of chemical science including the interfaces with related disciplines such as biology, medicine and materials science. The goal of each thematic volume is to give the non-specialist reader, whether in academia or industry, a comprehensive insight into an area where new research is emerging which is of interest to a larger scientific audience. Each review within the volume critically surveys one aspect of that topic and places it within the context of the volume as a whole. The most significant developments of the last 5 to 10 years are presented using selected examples to illustrate the principles discussed. The coverage is not intended to be an exhaustive summary of the field or include large quantities of data, but should rather be conceptual, concentrating on the methodological thinking that will allow the non-specialist reader to understand the information presented. Contributions also offer an outlook on potential future developments in the field. The chapter "Lignin-Based Composite Materials for Photocatalysis and Photovoltaics" is available open access under a CC BY 4.0 License via link.springer.com.

It's time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you're grokking as you go. You'll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features * Different types of machine learning, including supervised and unsupervised learning * Algorithms for simplifying, classifying, and splitting data * Machine learning packages and tools * Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.