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

In unsupervised learning, descriptive models are used for exploratory analysis to uncover patterns in unlabeled data. Examples of unsupervised learning tasks include algorithms for clustering and those for dimension reduction. In clustering, observations are assigned to groups in which there is high within-group homogeneity and between-group heterogeneity. Simply put, observations are placed into clusters of samples with other observations that are very similar. Use cases for clustering algorithms are vast. For example, analysts seeking to elevate sales by targeting selected customers for marketing advertisements and promotions separate customers by their shopping behavior.

Note

Additionally, hierarchical clustering has been implemented in academic neuroscience and motor behavior research (https://www.researchgate.net/profile/Ming-Yang_Cheng/project/The-Effect-of-SMR-Neurofeedback-Training-on-Mental-Representation-and-Golf-Putting-Performance/attachment/57c8419808aeef0362ac36a5...