BPB Online LLP
Demystifying Artificial intelligence
Demystifying Artificial intelligence
US$ 19.95
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Description
Contents
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Learn AI & Machine Learning from the first principles.

Key Features
Explore how different industries are using AI and ML for diverse use-cases.
Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner.
Cutting-edge coverage on use of ML for business products and services.
Explore how different companies are monetizing AI and ML technologies.
Learn how you can start your own journey in the AI field from scratch.

Description
AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know.

So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit.

Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others.

In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits.

What you will learn
Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words
How you can leverage and integrate AI technologies in your business to differentiate your product in the market.
The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs.
How AI can help companies in launching new products, improving existing ones and automating mundane processes.
Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users.

Who this book is for
This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML.

Table of Contents
1.Introduction
2. Going deeper in ML concepts
3. Business perspective of AI
4. How to get started and pitfalls to avoid

About the Authors
Prashant Kikani is an experienced data scientist, who ranks in the top 1% worldwide in competitions and kernels on Kaggle which is the world's largest community and platform for data science and machine learning.

As part of his day-to-day work, he is working on solving some of the hardest problems for the human kind, like language translation using state-of-the-art deep learning-based NLP models and infusion of knowledge graphs in NLP models. He is one of the youngest students to achieve the Master title on the Kaggle kernel platform. Also, he has worked on other deep learning sub-fields like computer vision via Kaggle competitions.
His interests lie in AI/ML and deep learning, and teaching others what he has learned in a very simple and intuitive manner. This book is part of his interest to share his knowledge in the simplest possible manner with everyone so that everyone they can learn about this fascinating technology called AI!
Blog links:http://prashantkikani.com
LinkedIn Profile://https://www.linkedin.com/in/prashant-kikani/

Language
English
ISBN
9789389898705
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewers
Acknowledgement
Preface
Errata
Table of Contents
1. Introduction
Structure
Objectives
What is data?
1. Text
2. Images
3. Audio
What is artificial intelligence (AI)?
Types of artificial intelligence
1. Narrow AI
2. General AI
What is machine learning (ML)?
Conversation time
1. Supervised machine learning
2. Unsupervised machine learning
What is deep learning?
What’s a neuron then?
What is “deep” in deep learning?
Advantages of deep learning
Limitations of deep learning
What is data science?
Why do we need artificial intelligence (AI)?
Automation
Conclusion
Questions
2. Going Deeper into ML Concepts
Structure
Objectives
Machine learning and Maths
Why is ML dependent on Maths?
Types of machine learning
1. Supervised machine learning
Conversation time
2. Unsupervised machine learning
Conversation time
Problem types in supervised learning
1. Classification type problems
Conversation time
2. Regression type problems
Conversation time
Problem types in unsupervised learning
1. Clustering
2. Dimensionality reduction
Semi-supervised learning
Self-supervised learning
How does a machine “learn” to perform a task?
1. Overfitting
Conversation time
2. Under-fitting
How does a machine “learn” to do things?
What is loss and loss function?
Conversation time
How does the neural network “learn” to perform a task?
What are “weights” in neural networks?
Why do we need a “deep” network or multiple layers in a network?
What is reinforcement learning (RL)?
What is the “reward” in RL?
Conversation time
Real-world examples
1. Gaming
2. Chemistry reactions
What is transfer learning?
Why do we do it?
Transfer learning in deep learning
Conversation time
What is computer vision?
1. Image classification
2. Image detection
3. Image segmentation
Downsides/flaws of computer vision
1. Adversarial attack
What is natural language processing (NLP)?
Attention mechanism in NLP
1. Text cleaning and preprocessing
2. Building ML models
Genetic algorithms in ML
1. Population
2. Fitness calculation
3. Parent selection
4. Crossover
5. Mutation
6. Offspring
Real-world applications
1. Medical and health care
2. Vehicle routing problems
Generative adversarial networks (GANs)
Conversation time
Recommendation
How do recommendations work?
Conclusion
Questions
3. Business Perspective of AI
Structure
Objectives
How do AI/ML projects work in the real world?
1. Deciding the task
2. Collecting data
3. Pre-processing data
What is feature engineering?
4. Training the computer/model
5. Checking whether the model has learned what we wanted it to or not
Conversation time
6. Use a trained model for future unseen test data
How to monetize AI?
How can one company/individual monetize AI?
Traditional businesses
Limitations of traditional non-tech businesses
Solutions of problems for traditional businesses
After achieving the growth path, how to maintain it?
How can you leverage AI in your business?
High quantity and quality data
Best talent who knows AI
Domain expertise
How to use AI in your existing products?
Let’s take a look at each of them in detail.
Automating the most time and cost consuming part of the process
Increasing sales with the product using data we already have
Demand forecasting
Dynamic pricing
Adding new AI-backed features in our existing products
Real-world use cases of AI
Netflix recommendation
Self-driving vehicles
How self-driving vehicles work?
Limitations and advantages of machine learning
Advantages
Automation
Speed
Performance
Limitations
One machine can’t do it all (i.e., one model can’t do multiple tasks)
Lake of explainability (also known as trust deficit)
It’s not that hard to fool the machine
Conclusion
Questions
4. How to Get Started, and Pitfalls to Avoid in AI
Structure
Objectives
How to get started in AI and machine learning?
How to start?
How to grow?
How to maintain growth?
A realistic view of artificial intelligence (AI)
Artificial intelligence and employment
Loss of jobs
1. Self-driving vehicles
2. Transportation
3. Education
4. Customer service/experience
5. Defense and security
Pitfalls to avoid in artificial intelligence
1. AI is not magic
2. Performance of AI models will degrade over time
3. Biases in our data can sometimes cause serious problems
4. Human help is still required
5. AI field is at a very early stage
Conclusion
Questions
Quiz time!
Answers for the quiz
References
Index

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