Artificial Intelligence and Machine Learning 2.0: With Natural Language Processing using Python 3.7

How to Use This Book
This book is for Students, data scientists, machine learning and artificial intelligence experts and professionals, and researchers in academia who want to understand the understanding of machine learning approaches/algorithms and artificial intelligence in practice using Python 3.7. This book presents some common machine learning and artificial intelligence associated technologies and their relationship. It will help the reader grab some important concepts.

Table of Contents
1.Artificial Intelligence and Machine Learning 2.0
2.Machine Learning 2.0 and Algorithms
3.Machine Learning and Artificial Intelligence in Market Research
4.Artificial Intelligence: The New AI Rising
5.Artificial Intelligence Applications
6.The Rise of Machine Learning 2.0
7.Machine Learning, Statistics, and Data Analytics
8.Natural Language Processing
9.Career Opportunities in Machine Learning
10.Artificial Intelligence and Smart Cities
11.Applications of Machine Learning
12.Make Them Smart with Machine learning
13.The Internet of Things
14.Chatbots using Python
15.Data and Artificial Intelligence
16.Artificial Intelligence Data Processing using Python 3.7
17.AWS and Artificial Intelligence
18.Recurrent Neural Networks
19.Working with Deep Q-Networks
20.Conclusion

Artificial intelligence and Machine Learning 2.0
Machine learning has a strong scientific foundation, which includes studies of pattern reorganization, mathematical optimization, computational learning theory, self-optimization, nature-inspired algorithms, and others.

We are using it for online entertainment (Netflix), practical speech recognition (Apple’s Siri), effective web searching, and improving our understanding of the human genome. Machine learning answered the questions of how to build intelligent computers and software that improve their capabilities by themselves through self-learning and assist humans in all walks of life.
Machine learning will help us make sense of an increasingly complex world. Already we are exposed to more data than what our sensors can cope with or our brains can process. Information repositories available online today contain massive amounts of digital text and are now so big that they cannot be processed manually.

Artificial Intelligence: The New AI Rising
Artificial intelligence (AI) is a broad field of study encompassing this complex problem solving and the human-like ability to sense, act, and reason. One goal of AI can be to create smart machines that think and act like humans, with the ability to simulate intelligence and produce decisions through processes in a similar manner to human reasoning.

When do we apply Machine Learning?
• When the system needs to be dynamic, self-learning and adaptive.
• At the time of multiple iterative and complex procedures.
• If the decision has to be taken instantly and real time.
• When we have complex multiple sources and a huge amount of
time series data.

Natural Language Processing
The area that focuses on making machines learn and understand the textual data in order to perform some useful tasks is known as Natural Language Processing (NLP). The text data could be structured or unstructured, and we have to apply multiple steps in order to make its analysis ready. NLP is already a huge contributor to multiple applications. There are many applications of NLP that are heavily used by businesses these days such as chatbot, speech recognition, language translation, recommender systems, spam detection, and sentiment analysis. This chapter demonstrates a series of steps in order to process text data and apply a Machine Learning Algorithm on it. It also showcases the sequence embeddings that can be used as an alternative to traditional input features for classification.