Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow,3rd Edition
by: Matthieu Brucher – Luis Pedro Coelho – Wilhelm Richert
ISBN-10: 1788623223
ISBN-13: 9781788623223
Publication Date 出版日期: 2018-07-31
Publisher finelybook 出版社: Packt
Print Length 页数: 406
Book Description
By finelybook
Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications,which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems.
Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python,being a dynamic language,allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You’ll quickly get to grips with serious,real-world projects on datasets,using modeling and creating recommendation systems. With Building Machine Learning Systems with Python,you’ll gain the tools and understanding required to build your own systems,all tailored to solve real-world data analysis problems.
By the end of this book,you will be able to build machine learning systems using techniques and methodologies such as classification,sentiment analysis,computer vision,reinforcement learning,and neural networks.
Contents
1: GETTING STARTED WITH PYTHON MACHINE LEARNING
2: CLASSIFYING WITH REAL-WORLD EXAMPLES
3: REGRESSION
4: CLASSIFICATION I – DETECTING POOR ANSWERS
5: DIMENSIONALITY REDUCTION
6: CLUSTERING – FINDING RELATED POSTS
7: RECOMMENDATIONS
8: ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING
9: CLASSIFICATION II – SENTIMENT ANALYSIS
10: TOPIC MODELING
11: CLASSIFICATION III – MUSIC GENRE CLASSIFICATION
12: COMPUTER VISION
13: REINFORCEMENT LEARNING
14: BIGGER DATA
What You Will Learn
Build a classification system that can be applied to text,images,and sound
Employ Amazon Web Services (AWS) to run analysis on the cloud
Solve problems related to regression using scikit-learn and TensorFlow
Recommend products to users based on their past purchases
Understand different ways to apply deep neural networks on structured data
Address recent developments in the field of computer vision and reinforcement learning
Authors
Luis Pedro Coelho
Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics―the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004,he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.
Willi Richert
Willi Richert has a PhD in machine learning/robotics,where he has used reinforcement learning,hidden Markov models,and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft,he is involved in various machine learning areas,such as deep learning,active learning,or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later,he discovered Turbo Pascal,then Java,then C++—only to finally arrive at his true love: Python.
