Название: Machine Learning: Concepts, Techniques and ApplicationsАвтор: T.V. Geetha, S. Sendhilkumar
Издательство: CRC Press
Год: 2023
Страниц: 478
Язык: английский
Формат: pdf (true)
Размер: 36.5 MB
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining Machine Learning and goes on to explain the basis of Machine Learning algorithms. The mathematical foundations required are outlined along with their associations to Machine Learning. The book then goes on to describe important Machine Learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical Machine Learning has been discussed. An outline of Deep Learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding.
Features:
Concepts of Machine Learning from basics to algorithms to implementation
Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers
Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications
Ethics of Machine Learning including Bias, Fairness, Trust, Responsibility
Basics of Deep learning, important Deep Learning models and applications
Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises
The R language was basically developed by statisticians to help other statisticians and developers work faster and more efficiently with the data. By now, we know that Machine Learning is basically working with a large amount of data and statistics as a part of Data Science, so the use of the R language is always recommended. Therefore the R language is becoming handy for those working with Machine Learning, making tasks easier, faster, and more innovative. Here are some top advantages of the R language to implement a Machine Learning algorithm in R programming.
Advantages to Implement Machine Learning Using R Language:
• It provides good explanatory code. For example, if you are at the early stage of working with a Machine Learning project and you need to explain the work you do, it becomes easy to work with R language in comparison to Python language as it provides the proper statistical method to work with data with fewer lines of code.
• R language is perfect for data visualization. R language provides the best prototype to work with machine learning models.
• R language has the best tools and library packages to work with Machine Learning projects. Developers can use these packages to create the best pre-model, model, and post-model of the Machine Learning projects. Also, the packages for R are more advanced and extensive than for Python language, which makes it the first choice to work with Machine Learning projects.
The book aims to make the thinking of applications and problems in terms of Machine Learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate Machine Learning algorithms and do appropriate performance evaluation.
Table of Contents:
1. Introduction. 2. Understanding Machine Learning. 3. Mathematiccal Foundations and Machine Learning. 4. Foundations and categoris of Machine Learning Techniques. 5. Machine Learning: Tool and Software 6. Classification Algorithms. 7. Probabilistic and Regression based approaches. 8. Performance Evaluation & Ensemble Methods. 9. Unsupervised Learning. 10. Sequence Models. 11. Reinforcement Learning. 12. Machine Learning Applications – Approaches. 13. Domain based Machine Learning Applications. 14. Ethical Aspects of Machine Learning. 15. Introduction to Deep Learning and Convolutional Neural Networks. 16. Other Models of Deep Learning and Applications of Deep Learning.
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