The “Linear Algebra for Computational Sciences and Engineering, 2nd Edition” is a must-read book for undergraduate engineering students. Ferrante Neri is the author of this informative book. Ferrante received a Master’s degree and a Ph.D. in Electrical Engineering from the Technical University of Bari, Italy, in 2002 and 2007 respectively. He received a DSc degree in Computational Intelligence in 2010. He was appointed Assistant Professor at the Department of Mathematical Information Technology at the University of Jyvaskyla. In this book, Ferrante Neri describes the main concepts of linear algebra from the viewpoint of applied scientists.

Mastering Machine Learning Algorithms is the design, machine theory, programming algorithm, and data modeling book that teaches students to master machine learning. Giuseppe Bonaccorso is the author of this remarkable book. It is an updated, revised, and new edition of the book that is based on research. He is the bestselling author in the New York Times. This book brings an opportunity to master and explore the most essential algorithms for solving complex machine learning challenges. Learn about the latest algorithms and how you can use them for the betterment of humanity. It briefly describes cutting edge applications, time series analysis, regression analysis, and deep learning models.

The “Algorithms of the Intelligent Web” guides you through algorithms to capture, store and structure data streams that come from the web. The authors of this most informative book are Douglas Mcllwraith, Haralambos Marmanis and Dmitry Babenko. Douglas Mcllwraith is a machine learning expert and data science practitioner in the field of online advertising. He currently working as a senior data scientist for a London-based advertising company. Dr.Haralambos Marmanis is the explorer of machine learning techniques for industrial solutions. Dmitry Babenko has designed a variety of applications and infrastructure frameworks for banking. Dmitry received an M.S degree in computer science from Belarussian University. This book teaches you through algorithm, store, capture and structure data streams coming from the web. This book includes the full introduction of machine learning, Extraction structure from data. Furthermore, how recommendation engines work and Deep learning, neural networks. All in all, Algorithms of the Intelligent Web teaches you how to create a machine learning application in the easiest way.

The “Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems” is a great book that reveals how to use genetic algorithms to improve your machine learning and deep learning models. Eyal Wirsansky is the author of this book. Eyal Wirsansky is a senior software engineer. He is a technology community leader and an artificial intelligence enthusiast and researcher. This book is very helpful for python users and artificial intelligence as it guides how to apply genetic algorithms to a wide range of tasks using Python. Eyal tells how to apply genetic algorithms to reinforcement learning tasks using OpenAI Gym. Additionally, this book enhances the performance of machine learning models and optimizes deep learning network architecture. After reading this book, you will have the experience of applying genetic algorithms in artificial intelligence. Hands-On Genetic Algorithms with Python is very helpful for all the programming students as it reveals everything in-depth with numerous examples and screenshots. All in all, Hands-On Genetic Algorithms with Python is a must-read book for software developers, data scientists, and AI developers.

Practical Machine Learning in R is the machine learning, data mining and artificial intelligence book that briefly explains machine learning in R. Fred Nwanganga is the author of this tremendous book. Machine learning is the branch of artificial language that takes technology to the next level. It is a professional guide for students to learn step by step and become master on ML. There are examples and hands-on exercises that take the student through every little step. Clear the concepts and enhance your experience in the field of AI. Machine Learning plays an important role whenever it comes to decision making. There are processes and techniques that are easy to apply through R. Find and explore data management techniques, dimensionality reduction, data collection, and exploration. It explains how to choose the right model, evaluate the performance, and enhance the data performance in an easy and accessible way. Learn about supervised and unsupervised learning and how to identify the difference between them. What is the role of clustering, apriori, and eclat? Discover the principles that are working behind Naïve Bayes classification approaches, Nearest Neighbor, and Decision Tree. It is a comprehensive guide for data scientists and business analysts.

Python Artificial Intelligence Projects for Beginners is the machine theory, natural language, semantics, and AI book for the python developers. Joshua Eckroth is the author of this remarkable book. It is time to build smart applications by implementing AI in the real world. This book brings an opportunity for python developers who want to initiate their career in artificial intelligence. It takes the reader step by step into the AI with easy to follow strategies. Joshua will teach you how to build your own prediction models and become on the top of your code. How to do prediction with random forests. What do you mean by random forests? Learn about deep learning and neural networks. How to use decision trees to generate maximum productivity. Discover the best way to identify handwritten and mathematical symbols by using neural networks. How to identify positive and negative sentiment. Analyze the performance of students and perform many other functionalities just in few clicks. AI is an advanced technology in the market that offers sectors, businesses, and industries. Learn how to create and use algorithms for advancement.