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)

Multiple Linear Regression

Multiple linear regression models define the relationship between two or more features and the continuous outcome variable using y = α + β1xi1 + β2xi2 + … + βp-1xi,p-1. Again, α represents the intercept and β denotes the slope for each feature (x) in the model. Thus, if we are predicting the weight of an individual in kg using height in m, total cholesterol in milligrams per deciliter (mg/dL), and minutes of cardiovascular exercise per day, and the multiple linear regression model computes 1.5 as the value for α, 50 as the coefficient for β1, 0.1 as the coefficient for β2, and -0.4 as the coefficient for β3, this model can be interpreted as for every 1 m increase in height, weight increases by 50 kg, controlling for all other features in the model. Additionally, for every 1 mg/dL increase in total cholesterol, weight increases by 0.1 kg, controlling for all other features in the model. Lastly, for...