Book Image

Python: Deeper Insights into Machine Learning

By : David Julian, Sebastian Raschka, John Hearty
Book Image

Python: Deeper Insights into Machine Learning

By: David Julian, Sebastian Raschka, John Hearty

Overview of this book

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.
Table of Contents (6 chapters)
4
A. Biblography
5
Index

Chapter 3. Turning Data into Information

Raw data can be in many different formats and of varying quantity and quality. Sometimes, we are overwhelmed with data, and sometimes we struggle to get every last drop of information from our data. For data to become information, it requires some meaningful structure. We often have to deal with incompatible formats, inconsistencies, errors, and missing data. It is important to be able to access different parts of the dataset or extract subsets of the data based on some relational criteria. We need to spot patterns in our data and get a feel for how the data is distributed. We can use many tools to find this information hidden in data from visualizations, running algorithms, or just looking at the data in a spreadsheet.

In this chapter, we are going to introduce the following broad topics:

  • Big data
  • Data properties
  • Data sources
  • Data processing and analysis

But first, let's take a look into the following explanations:

What is data?

Data can...