Recently, Huang Xiaoxi began to contact some algorithm work, so quickly find a number of machine learning books to learn, from research and development to algorithm, it is really a completely different feeling. If you get tired of writing code, learn about algorithms, starting with machine learning, and open the door to AI learning.
Machine learning practical book list
Machine learning
Machine learning research in the field of artificial intelligence is an important research direction, in the context of the current era of big data, capture data and to extract valuable information or pattern, become the industry strives for the survival and development of the determinant method, which makes the past by analysts and mathematician exclusive area of research is more and more attention by people.
“Machine Learning in Action” mainly introduces the fundamentals of machine learning, and how to use algorithms to classify, and gradually introduces a variety of classic supervised learning algorithms. Such as K-nearest neighbor algorithm, naive Bayes algorithm, Logistic regression algorithm, support vector machine, AdaBoost integration method, tree-based regression algorithm and classification regression tree (CART) algorithm, etc. The third part focuses on unsupervised learning and its main algorithms: K-means clustering algorithm, Apriori algorithm, FP-growth algorithm. The fourth part introduces some auxiliary tools of machine learning algorithm.
Machine Learning in Action cuts to everyday tasks with carefully choreographed examples, abandons academic language, and uses efficient reusable Python code to illustrate how to process, analyze, and visualize statistics. Through various examples, readers can learn the core algorithms of machine learning and apply them to strategic tasks such as classification, prediction, and recommendation. In addition, they can be used for more advanced functions such as summarization and simplification.
Author’s brief introduction
Peter Harrington holds a BACHELOR’s and Master’s degrees in Electrical Engineering. He has worked for Intel Corporation in California and China for 7 years. Peter holds five US patents and has published in three academic journals. Prior to joining Zillabyte, where he is now chief Scientist, He spent two years as a machine learning software consultant. Peter also participates in programming competitions and builds 3D printers in his spare time.
Machine learning principles, algorithms and Applications
Machine learning is currently the core technology to solve many AI problems. Since 2012, the emergence of deep learning has brought about a Renaissance of AI. This book is an introductory and improved textbook in the field of machine learning and deep learning. It closely combines engineering practice and application, and systematically and deeply tells the mainstream methods and theories of machine learning and deep learning.
The theoretical derivation and proof of this book are detailed and in-depth, with a clear structure. The principle and details of the main algorithm are described in detail, so that readers can not only know how it is, but also know why it is, and truly understand the algorithm and learn to use the algorithm. For undergraduate and graduate students majoring in computer, artificial intelligence and related majors, this is a textbook suitable for introduction and systematic learning. The book is also of great reference value to engineers engaged in the development of artificial intelligence and machine learning products.
Author’s brief introduction
Lei Ming, founder of SIGAI, is committed to the development of machine learning and deep learning, computer vision framework. She received her MASTER’s degree from The Department of Computer Science, Tsinghua University in 2009. Her research interests include machine learning and computer vision, and she has published several papers. He worked at Baidu As a senior software engineer and project manager. Zmodo/Meshare is the director of the CTO and Platform Development Center. He has rich academic research and product development experience in machine learning and computer vision.
Scikit-learn machine learning
Machine learning is a very popular technology. This book covers a wide range of machine learning models, including popular machine learning algorithms such as K-nearest Neighbor, logistic regression, Naive Bayes, K-means, decision trees, and artificial neural networks. At the same time, topics such as data preprocessing, hyperparameter optimization and integration methods are also discussed.
By the end of this book, you will have learned how to build systems for tasks such as document classification, image recognition, and AD detection. You will also have learned how to use the SCIKit-Learn API library to extract features from category variables, text, and images, how to evaluate the performance of models, and how to develop intuition about how to improve the performance of models.
In addition, you will learn the skills needed to build efficient models using SciKit-Learn in the field and be able to accomplish advanced tasks with practical strategies.
In recent years, Python has become a popular programming language, and it is well represented in the field of machine learning. Scikit-learn is a machine learning algorithm library written in Python that implements a range of commonly used machine learning algorithms and is a good tool.
Author’s brief introduction
Gavin Hackeling is a data scientist and author. He has worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. Gavin Hackeling is a graduate of the University of North Carolina and New York University and currently lives in Brooklyn with his wife and cat.
