Book Image

Machine Learning for Time-Series with Python

By : Ben Auffarth
Book Image

Machine Learning for Time-Series with Python

By: Ben Auffarth

Overview of this book

The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.
Table of Contents (15 chapters)
13
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14
Index

Introduction to reinforcement learning

Reinforcement learning is one of the main paradigms in machine learning alongside supervised and unsupervised methods. A major distinction is that supervised or unsupervised methods are passive, responding to changes, whereas RL is actively changing the environment and seeking out new data. In fact, from a machine learning perspective, reinforcement learning algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

Computer programs based on reinforcement learning have been breaking through barriers. In a watershed moment for artificial intelligence, in March 2016, DeepMind's AlphaGo defeated the professional Go board game player Lee Sedol. Previously, the game of Go was considered to be a hallmark of human creativity and intelligence, too complex to be learned by a machine.

It has been argued that it is edging us closer toward Artificial General Intelligence (AGI). For example...