The lack of understanding of the math behind machine learning doesn’t make you a professional in this field, so that you can take this book o understand it well. You will start to understand the basics of math implemented in machine learning, such as linear algebra and analytic geometry. Y...
Not a book, but a great place you can start out is the Machine Learning and Statistical Learning view on CRAN maintained by Torsten Hothorn. It lists most of the R packages you can use for machine learning, grouped by algorithm and algorithm types. It is a great place to start, but on...
Machine Learning course offered by AI visionary Andrew Ng has been rebuilt and expanded into this ML specialization comprising of 3 courses, that teach foundational AI concepts through an intuitive visual approach, and introduce the code needed to implement the algorithms and the underlying math. ...
After reading the book, you’ll be ready to discuss all kinds of topics related to machine learning, including supervised and unsupervised learning, the most popular machine learning algorithms, and what it takes to build and fine-tune a model. Math, intuition, and illustrations, all in just ...
application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.” ...
for Machine Learning comes to the rescue. The intent of this book is not only to talk about advanced machine learning techniques but to deliver essential mathematical skills. So, every beginner will grasp the concept easily. It will be one of the machine learning best books for you if you ...
It is machine-independent, structured programming language which is used extensively in various applications. Are you interested in learning the C Language and looking for some excellent book that will help you skyrocket your C programming expertise? Then you have come to the right place. Read ...
book provides a wide range of role for data scientists and software engineers with experience in machine learning. You will start with the basics of deep learning and quickly move on to the details of important advanced architecture by implementing everything from the beginning. This book provides...
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Published in 2016,Weapons of Math Destructionpaved the way for a necessary debate about the ethical implications of big data. According to Cathy O’Neill, algorithms are perpetuating harmful biases. Illustrating these biases with real examples, O’Neill ends the book by arguing how transparency and...