Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition) Hardcover - 17 Nov 2014
by Stephen Marsland
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition
- Two new chapters on deep belief networks and Gaussian processes
- Reorganization of the chapters to make a more natural flow of content
- Revision of the support vector machine material, including a simple implementation for experiments
- New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
- Additional discussions of the Kalman and particle filters
- Improved code, including better use of naming conventions in Python
Python Machine Learning: A Technical Approach To Python Machine Learning For Beginners Paperback - March 9, 2018
by Leonard Eddison
What exactly is machine learning and why is it so valuable in the online business world? Simply put, it is a method of data analysis that uses algorithms that learn from data and produce specific results without being specifically programmed to do so. These algorithms can analyze data, calculate how frequently certain parts of it are used and generate responses based on these calculations in order to automatically interact with users.
The main topics:
- What does Machine Learning mean Machine Learning And Artificial Intelligence Some of the branches of Artificial Intelligence Decision trees in relation to Machine Learning What Python is and how to get started with it What input and output mean in Python The way that Python evolved throughout time
- After reading this book, you will be able to write simple codes using Python. You will also know the direction that you should go after you have surpassed the beginner level of Python.
Understanding Machine Learning: From Theory to Algorithms 1st Edition, Kindle Edition
by Shai Shalev-Shwartz
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.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Kindle Edition
by Sebastian Raschka
- The second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open-source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.