An Introduction to Machine Learning with Python: Step-By-Step Guide To Learning And Understanding Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts and Techniques
Machine Learning with Python:
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Are you a novice trying to understand the basics of machines?
Do you have prior knowledge and wish to acquire further understanding about TensorFlow, sci-kit-learn, algorithms, decision trees, random forest, deep learning, or neural networks?
Are you even a pro and wish to add to your knowledge?