Book Image

Data Science with Python

By : Rohan Chopra, England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, 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 to Data Science and Data Pre-Processing

Learning Objectives

By the end of this chapter, you will be able to:

  • Use various Python machine learning libraries
  • Handle missing data and deal with outliers
  • Perform data integration to bring together data from different sources
  • Perform data transformation to convert data into a machine-readable form
  • Scale data to avoid problems with values of different magnitudes
  • Split data into train and test datasets
  • Describe the different types of machine learning
  • Describe the different performance measures of a machine learning model

This chapter introduces data science and covers the various processes included in the building of machine learning models, with a particular focus on pre-processing.