Practical Machine Learning -: Experimental algorithms and techniques for machine learning and artificial intelligence

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge techniques About This Book * Broad and comprehensive practical solutions taking you into the future of machine learning. *Take step forward and start integrating your machine learning projects with Hadoop and Spark. *Tested and full coded working examples using a wide range of machine learning libraries and tools including R, Python, Scala, Julia, and Spark. Who This Book Is For This book appeals to aspiring data science and machine learning professionals who want to see Machine Learning in action and explore its real-world applications. Readers are expected to have basic programming skills and basic familiarity with statistics would be an added advantage. What You Will Learn * Implement a wide range of algorithms and techniques for tackling complex data *Get to grips with some of the most powerful languages in data science, including R, Python, and Julia *Harness the capabilities of Spark and Hadoop to manage and process data successfully *Apply the appropriate machine learning technique to address real-world problems *Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning *Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Machine learning gains the popularity not because it is done with a particular language or technique, it appeals everyone due to the ease it brings to everyone's life. It makes data much more accessible and simpler to understand and gain hidden insights from data. This book focuses on exploring all the Machine Learning techniques and some specific behavioral differences or implementation intricacies with the parallel or distributed processing approach. For each technique along with a deep dive on internals of each algorithm, example implementations using top and evolving machine learning frameworks and tools like R, SPSS, Python, Scala, Julia and Spark. This book helps readers master Machine Learning techniques and gain ability to identify and apply appropriate techniques in the given problem context. In the context of large datasets, multi-core cluster based learning, distributed learning, parallel computation tools and libraries and more. Readers will be exposed to a list of machine learning frameworks and for each of the frameworks detailed implementation aspects like function libraries, syntax, installation or set-up and integration with Hadoop and Spark. The book also explores cutting-edge advances in machine learning, with worked examples and guidance on deep learning—TensorFlow and Keras-- and reinforcement learning, providing you with practical demonstrations and samples that help take the theory—and—mystery out of even the most advanced machine learning methodologies.

About the Author :
Sunila Gollapudi works as Vice President Technology with Broadridge Financial Solutions (India) Pvt. Ltd., a wholly owned subsidiary of the US-based Broadridge Financial Solutions Inc. (BR). She has close to 14 years of rich hands-on experience in the IT services space. She currently runs the Architecture Center of Excellence from India and plays a key role in the big data and data science initiatives. Prior to joining Broadridge she held key positions at leading global organizations and specializes in Java, distributed architecture, big data technologies, advanced analytics, Machine learning, semantic technologies, and data integration tools. Sunila represents Broadridge in global technology leadership and innovation forums, the most recent being at IEEE for her work on semantic technologies and its role in business data lakes. Sunila's signature strength is her ability to stay connected with ever changing global technology landscape where new technologies mushroom rapidly, connect the dots and architect practical solutions for business delivery. A post graduate in computer science, her first publication was on Big Data Datawarehouse solution, Greenplum titled Getting Started with Greenplum for Big Data Analytics, Packt Publishing. She's a noted Indian classical dancer at both national and international levels, a painting artist, in addition to being a mother, and a wife. Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnologica Nacional, Argentina. He has also pursued parallel programming and image understanding postgraduate courses at Universitat Stuttgart, Germany. He has been doing research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU- and GPU-supporting neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks and is currently working on signal classification using machine learning techniques. He is also the author of Building Machine Learning Projects with Tensorflow and Machine Learning for Developers by Packt Publishing.