Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

K-means Clustering

Like HCA, K-means also uses distance to assign observations into clusters not labeled in data. However, rather than linking observations to each other as in HCA, k-means assigns observations to k (user-defined number) clusters.

To determine the cluster to which each observation belongs, k cluster centers are randomly generated, and observations are assigned to the cluster in which its Euclidean distance is closest to the cluster center. Like the starting weights in artificial neural networks, cluster centers are initialized at random. After cluster centers have been randomly generated there are two phases:

  • Assignment phase
  • Updating phase


    The randomly generated cluster centers are important to remember, and we will be visiting it later in this chapter. Some refer to this random generation of cluster centers as a weakness of the algorithm, because results vary between fitting the same model on the same data, and it is not guaranteed to assign observations to the...