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)

Data Augmentation

While training machine learning models, we data scientists often run into the problem of imbalanced classes and a lack of training data. This leads to sub-par models that perform poorly when deployed in real-life scenarios. One easy way to deal with these problems is data augmentation. There are multiple ways of performing data augmentation, such as rotating the image, shifting the object, cropping an image, shearing to distort the image, and zooming in to a part of the image, as well as more complex methods such as using Generative Adversarial Networks (GANs) to generate new images. GANs are simply two neural networks that are competing with each other. A generator network tries to make images that are similar to the already existing images, while a discriminator network tries to determine if the image was generated or was part of the original data. After the training is complete, the generator network is able to create images that are not a part of the original data but...