Machine learning is one of the principal areas of artificial intelligence.
It concerns the study and the development of quantitative models allowing a computer to accomplish tasks without it being explicitly programmed to do them. Learning in this context means recognizing complex shapes and making smart decisions. Given all the existing entries, the complexity of doing so lies in the fact that the set of possible decisions is usually very difficult to enumerate. The machine learning algorithms have, therefore, been designed to gain knowledge about the problem to be addressed based on a set of limited data from this problem.
- This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include:
- Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future!
Also, this book presents the scientific foundations of the theory of supervised learning, the most widespread algorithms developed in this field as well as the two frameworks of semi-supervised learning and scheduling, at a level accessible to master’s students and engineering students. We had here the concern to provide a coherent presentation linking the theory to the algorithms developed in this sphere. But this study is not limited to present these foundations; you will find some programs of classical algorithms proposed in this manuscript, written in C language (language both simple and popular), and for readers who want to know how it works. These models are sometimes referred to as black boxes.
Who is this book directed to:
The engineering students, master’s students, including doctoral students in applied mathematics, algorithmic, operations research, production management, decision support.
Also, to engineers, teacher-researchers, computer scientists, industrialists, economists, and decision-makers who have to resolve problems of classification, partitioning, and scheduling on a large scale.
In this book, you will attain helpful information for getting started, such as:
- Why Use Neural Networks?
- The Different Types Of Learning
- Machine Learning In Practice
- Reinforcement Learning
- Learning by reinforcement
- Neural Networks
- Tasks Of Neural Networks
- Neural Networks versus Conventional Computers
- Deep Learning
- Machine Learning and Data Mining
- Running Python Getting Started and more,
When you are ready to learn more about what machine learning is all about, and how you are able to benefit from it in your own coding and programming, make sure to check out this guidebook to help you get started.
Do not waste time to gather partial or false information, when you can get everything you require to REACH YOUR GOALS by reading this fantastic guide.
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