▶Book Description
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.
▶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
▶Key Features
⦁ Develop your own Python-based machine learning system
⦁ Discover how Python offers multiple algorithms for modern machine learning systems
⦁ Explore key Python machine learning libraries to implement in your projects
▶Who This Book Is For
Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.
▶What this book covers
⦁ Chapter 1, Getting Started with Python Machine Learning, introduces the basic idea of machine learning and TensorFlow with a very simple example. Despite its simplicity, it will challenge us with the risk of overfitting.
⦁ Chapter 2, Classifying with Real-world Examples, uses real data to explore classification by training a computer to be able to distinguish between different classes of flowers.
⦁ Chapter 3, Regression, explains how to use regression to handle data, a classic topic that is still relevant today. You will also learn about advanced regression techniques such as Lasso and ElasticNet.
⦁ Chapter 4, Classification I –. Detecting Poor Answers, demonstrates how to use the biasvariance trade-off to debug machine learning models, though this chapter is mainly about using logistic regression to ascertain whether a user's answer to a question is good or bad.
⦁ Chapter 5, Dimensionality Reduction, explores what other methods exist to help us to downsize data so that it is chewable by our machine learning algorithms.
⦁ Chapter 6, Clustering –. Finding Related Posts, demonstrates how powerful the bag of words approach is by applying it to find similar posts without really understanding them.
⦁ Chapter 7, Recommendations, builds recommendation systems based on customer product ratings. We will also see how to build recommendations from shopping data without the need for ratings data (which users do not always provide).
⦁ Chapter 8, Artificial Neural Networks and Deep Learning, deals with the fundamentals and examples of CNN and RNN using TensorFlow.
⦁ Chapter 9, Classification II –. Sentiment Analysis, explains how Naive Bayes works, and how to use it to classify tweets to see whether they are positive or negative.
⦁ Chapter 10, Topic Modeling, moves beyond assigning each post to a single cluster to assigning posts to several topics, as real texts can deal with multiple topics.
⦁ Chapter 11, Classification III –. Music Genre Classification, sets the scene of someone having scrambled our huge music collection, our only hope of creating order being to let a machine learner classify our songs. It turns out that it is sometimes better to trust someone else's expertise to create features ourselves. The chapter also covers the conversion of speech into text.
⦁ Chapter 12, Computer Vision, demonstrates how to apply classification in the specific context of handling images by extracting features from data. We also see how these methods can be adapted to find similar images in a collection, and the applications of CNN and GAN using TensorFlow.
⦁ Chapter 13, Reinforcement Learning, covers the fundamentals of reinforcement learning and Deep Q networks on Atari game playing.
⦁ Chapter 14, Bigger Data, explores some approaches to dealing with larger data by taking advantage of multiple cores or computing clusters. It also introduces cloud computing (using Amazon Web Services as our cloud provider).
