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)

Decision Trees

Imagine we are considering changing jobs. We are weighing the pros and cons of prospective job opportunities and, after a few years of being in our current position, we start to realize the things that are important to us. However, not all aspects of a career are of equal importance. In fact, after being in the job for a few years, we decide that the most important aspect of a position is our interest in the projects we will be doing, followed by compensation, then work-related stress, trailed by commute time, and, lastly, benefits. We have just created the scaffolding of a cognitive decision tree. We can go into further detail by saying that we want a job where we are very interested in the allocated projects, paying at least $55k/year, with low work-related stress, a commute of under 30 minutes, and good dental insurance. Creating mental decision trees is a decision-making process we all utilize by nature and is one of the reasons why decision trees are one of the most widely...