Artificial Intelligence and Data science

Book Description:

This Book explores the intersection of artificial intelligence (AI) and data science, which are two rapidly evolving fields that have significant impact on various industries. Readers will gain a deep understanding of the fundamentals of AI and data science, including machine learning, natural language processing, computer vision, big data analytics, and predictive modeling. Through hands-on projects and case studies, students will learn how to apply these concepts to real-world problems and gain practical skills in developing AI-powered solutions.

 

Reading Objectives:

1. Understand the basic principles and techniques of artificial intelligence
2. Explore different types of machine learning algorithms and their applications
3. Learn how to build predictive models using data science methods
4. Gain knowledge about natural language processing and its applications in AI
5. Develop skills in computer vision techniques for image analysis
6. Become familiar with big data analytics tools and techniques
7. Apply AI concepts to solve real-world problems through hands-on projects
8. Understand ethical considerations in the development and use of AI technologies

Table of contents

Module 1: Introduction to Artificial Intelligence

– Definition of AI

– History of AI

– Types of AI systems

– Applications of AI

 

Module 2: Machine Learning Basics

– Supervised vs Unsupervised learning

– Linear regression

– Decision trees

– Naive Bayes classifier

 

Module 3: Advanced Machine Learning Techniques

– Neural networks

– Support Vector Machines (SVMs)

– Ensemble methods

– Evaluation metrics

 

 

Module 4: Natural Language Processing (NLP) Fundamentals

– Understanding human language for computers

– NLP tasks such as sentiment analysis, text classification, etc.

– Techniques for text preprocessing

-Writing regular expressions

 

Module 5: Computer Vision Basics

– Image representation

– Feature extraction

– Convolutional neural networks

 

Module 6: Big Data Analytics

– Introduction to big data and its characteristics

– Data mining techniques

– Handling large datasets

– Tools for big data analytics

 

Module 7: Predictive Modeling with Data Science

– Understanding predictive modeling

– Developing a predictive model using Python or R

– Evaluating and optimizing models

Module 8: Ethical Considerations in AI Development

– Benefits and risks of AI technologies

– Bias in AI systems

– Ethical guidelines for responsible development and use of AI

 

Module 9: Case Studies

Case studies from various industries such as healthcare, finance, marketing, etc. will be discussed to showcase how AI and data science are being applied in different contexts.

 

Module 10: Questions and answer

 

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