Machine Learning: Fundamentals and Applications
ΠΡΠΈΡΠΊΠΎ Π·Π° ΡΠ°Π·ΠΈ Π΅Π»Π΅ΠΊΡΡΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°
Machine learning (ML) is a subfield of computer science that focuses on the study and development of methods that enable computers to "learn." These are methods that make use of data in order to enhance a computer's performance on a certain set of tasks.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Machine learning
Chapter 2: Big data
Chapter 3: Self-driving car
Chapter 4: Unsupervised learning
Chapter 5: Supervised learning
Chapter 6: Statistical learning theory
Chapter 7: Computational learning theory
Chapter 8: Automated machine learning
Chapter 9: Differentiable programming
Chapter 10: Reinforcement learning
(II) Answering the public top questions about machine learning.
(III) Real world examples for the usage of machine learning in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of machine learning' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of machine learning.
