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)

Introduction

One of the most important goals of artificial intelligence (AI) is to understand the human language to perform tasks. Spellcheck, sentiment analysis, question answering, chat bots, and virtual assistants (such as Siri and Google Assistant) all have a natural language processing (NLP) module. The NLP module enables virtual assistants to process human language and perform actions based on it. For example, when we say, "OK Google, set an alarm for 7 A.M.", the speech is first converted to text and then this text is processed by the NLP module. After this processing, the virtual assistant calls the appropriate API of the Alarm/Clock application. Processing human language has its own set of challenges because it is ambiguous, with words meaning different things depending on the context in which they are used. This is the biggest pain point of language for AI.

Another big reason is the unavailability of complete information. We tend to leave out most of the information while...