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)

Text Data Processing

Before we start building machine learning models for our textual data, we need to process the data. First, we will learn the different ways in which we can understand what the data comprises. This helps us get a sense of what the data really is and decide on the preprocessing techniques to be used in the next step. Next, we will move on to learn the techniques that will help us preprocess the data. This step helps reduce the size of the data, thus reducing the training time, and also helps us transform the data into a form that would be easier for machine learning algorithms to extract information from. Finally, we will learn how to convert the textual data to numbers so that machine learning algorithms can actually use it to create models. We do this using word embedding, much like the entity embedding we performed in Chapter 5: Mastering Structured Data.

Regular Expressions

Before we start working on textual data, we need to learn about regular expressions (RegEx)....