#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Building a recurrent neural network for sequential data analysis

Recurrent neural networks are really good at analyzing sequential and time-series data. You can learn more about them at http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns. When we deal with sequential and time-series data, we cannot just extend generic models. The temporal dependencies in the data are really important, and we need to account for this in our models. Let's look at how to build them.

### How to do it…

1. Create a new Python file, and import the following packages:

```import numpy as np
import matplotlib.pyplot as plt
import neurolab as nl```
2. Define a function to create a waveform, based on input parameters:

```def create_waveform(num_points):
# Create train samples
data1 = 1 * np.cos(np.arange(0, num_points))
data2 = 2 * np.cos(np.arange(0, num_points))
data3 = 3 * np.cos(np.arange(0, num_points))
data4 = 4 * np.cos(np.arange(0, num_points))```
3. Create different amplitudes...