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)

Unsupervised Learning

Unlike supervised learning, the unsupervised learning process involves data that is neither classified nor labeled. The algorithm will perform analysis on the data without guidance. The job of the machine is to group unclustered information according to similarities in the data. The aim is for the model to spot patterns in the data in order to give some insight into what the data is telling us and to make predictions.

An example is taking a whole load of unlabeled customer data and using it to find patterns to cluster customers into different groups. Different products could then be marketed to the different groups for maximum profitability.

Unsupervised learning is broadly categorized into two types:

  • Clustering: A clustering procedure helps to discover the inherent patterns in the data.
  • Association: An association rule is a unique way to find patterns associated with a large amount of data, such as the supposition that when someone buys product 1, they also tend to buy product 2.