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)

Max Margin Classification Using SVMs

SVM is an algorithm for supervised learning that solves both classification and regression problems. However, SVM is most commonly used in classification problems, so, for the purposes of this chapter, we will focus on SVM as a binary classifier. The goal of SVM is to determine the best location of a hyperplane that create a class boundary between data points plotted on a multidimensional space. To help clarify this concept, refer to Figure 3.20.

Figure 3.20: Hyperplane (blue) separating the circles from the squares in three dimensions

In Figure 3.20, the squares and circles are observations in the same DataFrame that represent different classes. In this figure, the hyperplane is depicted by a semi-transparent blue boundary lying between the circles and squares that separate the observations into two distinct classes. In this example, the observations are said to be linearly separable.

The location of the hyperplane is determined by finding...