You're reading from Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Length 618 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Prateek Joshi

Alberto Artasanchez

View More author details

Table of Contents (26) Chapters

Preface

1. Introduction to Artificial Intelligence

2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER

3. Machine Learning Pipelines

4. Feature Selection and Feature Engineering

5. Classification and Regression Using Supervised Learning

6. Predictive Analytics with Ensemble Learning

7. Detecting Patterns with Unsupervised Learning

8. Building Recommender Systems

9. Logic Programming

10. Heuristic Search Techniques

11. Genetic Algorithms and Genetic Programming

12. Artificial Intelligence on the Cloud

13. Building Games with Artificial Intelligence

14. Building a Speech Recognizer

15. Natural Language Processing

16. Chatbots

17. Sequential Data and Time Series Analysis

18. Image Recognition

19. Neural Networks

20. Deep Learning with Convolutional Neural Networks

21. Recurrent Neural Networks and Other Deep Learning Models

22. Creating Intelligent Agents with Reinforcement Learning

23. Artificial Intelligence and Big Data

24. Other Books You May Enjoy

25. Index

Data preparation

The next step is a data transformation tier that processes the raw data; some of the transformations that need to be done are:

Data Cleansing
Filtration
Aggregation
Augmentation
Consolidation
Storage

The cloud providers have become the major data science platforms. Some of the most popular stacks are built around:

Azure ML service
AWS SageMaker
GCP Cloud ML Engine
SAS
RapidMiner
Knime

One of the most popular tools to perform these transformations is Apache Spark, but it still needs a data store. For persistence, the most common solutions are:

Hadoop Distributed File System (HDFS)
HBase
Apache Cassandra
Amazon S3
Azure Blob Storage

It's also possible to process data for machine learning in-place, inside the database; databases like SQL Server and SQL Azure are adding specific machine learning functionality to support machine learning pipelines. Spark has that...