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Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

By : Joos Korstanje
4.2 (9)
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Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

4.2 (9)
By: Joos Korstanje

Overview of this book

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
Table of Contents (17 chapters)
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1
Part 1: Introduction and Core Concepts of Streaming Data
5
Part 2: Exploring Use Cases for Data Streaming
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
15
Chapter 12: Conclusion and Best Practices

Implementing reinforcement learning in Python

Let's now move on to an example in which streaming data is used for Q-Learning. The data that we will be using is simulated data of stock prices:

  1. The data is generated in the following block of code.

The list of values that is first generated is a list of 30,000 consecutive values that represent stock prices. The data generating process is the starting point of 0 and at every time step, there is a random value added to this. The random normal values are centered around 0, which indicates that prices would go up or down by a step size based on a standard deviation of 1.

This process is often referred to as a random walk and it can go quite far up or down. After that, the values are standardized to be within a normal distribution again.

Code Block 8-1

import numpy as np
import matplotlib.pyplot as plt
import random
starting = 0
values = [starting]
for i in range(30000):
    values.append(values...
CONTINUE READING
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Machine Learning for Streaming Data with Python
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