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)

Neural Networks

A neural network is one of the most popular machine learning algorithms available to data scientists. It has consistently outperformed traditional machine learning algorithms in problems where images or digital media are required to find the solution. Given enough data, it outperforms traditional machine learning algorithms in structured data problems. Neural networks that have more than 2 layers are referred to as deep neural networks and the process of using these "deep" networks to solve problems is referred to as deep learning. Two handle unstructured data there are two main types of neural networks: a convolutional neural network (CNN) can be used to process images and a recurrent neural network (RNN) can be used to process time series and natural language data. We will talk more about CNNs and RNNs in Chapter 6, Decoding Images and Chapter 7, Processing Human Language. Let us now see how a vanilla neural network really works. In this section, we will go over...