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Artificial Intelligence

Categories: Artificial Intelligence

About Course

Foundations of Artificial Intelligence: Delve into the fundamental concepts of artificial intelligence, including understanding the history of AI, its core principles, and key terminology.
Hands-On AI Projects: Apply your knowledge through hands-on projects that progressively increase in complexity. Learn to develop AI models and applications for real-world problems.

AI Tools and Frameworks: Familiarize yourself with essential AI tools and frameworks, such as TensorFlow and PyTorch. Gain proficiency in using these tools to build AI solutions.

AI Best Practices: Discover the importance of AI best practices, including data preparation, model evaluation, and ethical considerations in AI development.

AI Ethics and Responsible AI: Explore the ethical considerations and responsible AI practices, including fairness, transparency, and accountability in AI systems.

Machine Learning and Deep Learning: Dive into the world of machine learning and deep learning, understanding algorithms, training models, and their applications in various fields.

AI in Business and Industry: Learn how AI is transforming various industries, such as healthcare, finance, and transportation. Explore the potential applications of AI in your career or business.

Career Opportunities in AI: Gain insights into the various career paths and job opportunities in the field of artificial intelligence. Understand the skills and knowledge required for a successful AI career. Who Should Take This Course?

Beginners: This course is designed for those who have little to no prior knowledge of artificial intelligence. We will start from the basics and guide you throughout the learning journey. Aspiring AI Professionals: If you aspire to build a career in the field of artificial intelligence, this course serves as your foundation to understanding AI concepts and practices. Business Owners and Managers: For business owners and managers, understanding AI fundamentals is crucial in leveraging AI technologies to enhance business operations and decision-making. Tech Enthusiasts: If you’re passionate about technology and want to explore the world of AI, this course will equip you with the knowledge to grasp AI concepts and potentially develop AI solutions. Enroll Today and Dive into the World of Artificial Intelligence!

Don’t miss the opportunity to begin your journey into the exciting world of artificial intelligence. Enroll in our AI Fundamentals Course today and gain the knowledge and skills to harness the power of AI for various applications. Join our community of learners and start your AI journey with confidence!

Course Content

Intrduction of course
This is Introduction Video of Course

Introduction

02:01

Module 1: Introduction to Artificial Intelligence
• Understanding the Significance of Artificial Intelligence • A Brief History and Evolution of AI • Overview of Various Career Paths in Artificial Intelligence • Tips for Starting a Career in AI

Module 2: Fundamentals of Artificial Intelligence
• Defining Artificial Intelligence • Types of Artificial Intelligence: Narrow vs. General AI • Machine Learning vs. Deep Learning vs. Reinforcement Learning • Ethical Considerations in AI

Module 3: Machine Learning Basics
• Introduction to Machine Learning • Supervised Learning, Unsupervised Learning, and Reinforcement Learning • Key Machine Learning Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks) • Data Preparation and Feature Engineering

Module 4: Deep Learning
• Exploring Deep Learning • Neural Networks and Deep Learning Architectures • Convolutional Neural Networks (CNNs) for Image Analysis • Recurrent Neural Networks (RNNs) for Sequence Data • Training Deep Learning Models

Module 5: Natural Language Processing (NLP)
• Understanding Natural Language Processing • Text Preprocessing and Tokenization • Sentiment Analysis • Named Entity Recognition (NER) • Language Models (e.g., GPT-3, BERT)

Module 6: Computer Vision
• Introduction to Computer Vision • Image Processing Techniques • Object Detection and Image Classification • Applications of Computer Vision

Module 7: Ethics and Bias in AI
• Addressing Bias in AI • Fairness, Accountability, and Transparency in AI (FAT/ML) • Ethical AI Development Practices • Case Studies on AI Ethical Issues

Module 8: AI Tools and Frameworks
• Overview of AI Development Tools (e.g., TensorFlow, PyTorch) • Hands-on Introduction to AI Frameworks • Setting Up AI Development Environment

Module 9: AI Applications and Industries
• AI in Healthcare • AI in Finance • AI in Autonomous Vehicles • AI in Robotics • Emerging AI Trends

Module 10: Capstone Project
• Final Project: Building an AI Model or Application • Presentation of Projects • Peer Evaluation and Feedback • Preparing for AI Job Interviews

Student Ratings & Reviews

5.0

Total 1 Rating

1 Rating

0 Rating

Nutan Amru

11 months ago

was amazing experience to learn from thi splatform and agin alot of menaingful insights

A course by

yourjinnie0

Comprehensive Video Lessons: Access a library of video lessons covering key web development topics, taught by experienced instructors.
Interactive Coding Exercises: Practice what you've learned with hands-on coding exercises that reinforce your skills and understanding.
Project Templates: Download project templates to kickstart your web development projects and save time on design and coding.
Course Notes and Documentation: Access downloadable course notes and documentation for quick reference and deeper learning.
Quizzes and Assessments: Test your knowledge with quizzes and assessments to track your progress and ensure comprehension.
Community Support: Join our online community to connect with fellow learners, share insights, and seek help when needed.
Lifetime Access: Enjoy lifetime access to course materials, allowing you to learn at your own pace and revisit content whenever you like.
Certificate of Completion: Receive a certificate upon finishing the course, demonstrating your newfound web development skills.

Artificial Intelligence

About Course

Course Content

Intrduction of course
This is Introduction Video of Course

Introduction

Module 1: Introduction to Artificial Intelligence
• Understanding the Significance of Artificial Intelligence • A Brief History and Evolution of AI • Overview of Various Career Paths in Artificial Intelligence • Tips for Starting a Career in AI

Module 1

Module 2: Fundamentals of Artificial Intelligence
• Defining Artificial Intelligence • Types of Artificial Intelligence: Narrow vs. General AI • Machine Learning vs. Deep Learning vs. Reinforcement Learning • Ethical Considerations in AI

Module 2

Module 3: Machine Learning Basics
• Introduction to Machine Learning • Supervised Learning, Unsupervised Learning, and Reinforcement Learning • Key Machine Learning Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks) • Data Preparation and Feature Engineering

Module 3

Module 4: Deep Learning
• Exploring Deep Learning • Neural Networks and Deep Learning Architectures • Convolutional Neural Networks (CNNs) for Image Analysis • Recurrent Neural Networks (RNNs) for Sequence Data • Training Deep Learning Models

Module 4

Module 5: Natural Language Processing (NLP)
• Understanding Natural Language Processing • Text Preprocessing and Tokenization • Sentiment Analysis • Named Entity Recognition (NER) • Language Models (e.g., GPT-3, BERT)

Module 5

Module 6: Computer Vision
• Introduction to Computer Vision • Image Processing Techniques • Object Detection and Image Classification • Applications of Computer Vision

Module 6

Module 7: Ethics and Bias in AI
• Addressing Bias in AI • Fairness, Accountability, and Transparency in AI (FAT/ML) • Ethical AI Development Practices • Case Studies on AI Ethical Issues

Module 7

Module 8: AI Tools and Frameworks
• Overview of AI Development Tools (e.g., TensorFlow, PyTorch) • Hands-on Introduction to AI Frameworks • Setting Up AI Development Environment

Module 8

Module 9: AI Applications and Industries
• AI in Healthcare • AI in Finance • AI in Autonomous Vehicles • AI in Robotics • Emerging AI Trends

Module 9

Module 10: Capstone Project
• Final Project: Building an AI Model or Application • Presentation of Projects • Peer Evaluation and Feedback • Preparing for AI Job Interviews

Module 10

About Us

Internships

Useful Links

Social Media

About Course

Course Content

Intrduction of course This is Introduction Video of Course

Introduction

Module 1: Introduction to Artificial Intelligence • Understanding the Significance of Artificial Intelligence • A Brief History and Evolution of AI • Overview of Various Career Paths in Artificial Intelligence • Tips for Starting a Career in AI

Module 1

Module 2: Fundamentals of Artificial Intelligence • Defining Artificial Intelligence • Types of Artificial Intelligence: Narrow vs. General AI • Machine Learning vs. Deep Learning vs. Reinforcement Learning • Ethical Considerations in AI

Module 2

Module 3: Machine Learning Basics • Introduction to Machine Learning • Supervised Learning, Unsupervised Learning, and Reinforcement Learning • Key Machine Learning Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks) • Data Preparation and Feature Engineering

Module 3

Module 4: Deep Learning • Exploring Deep Learning • Neural Networks and Deep Learning Architectures • Convolutional Neural Networks (CNNs) for Image Analysis • Recurrent Neural Networks (RNNs) for Sequence Data • Training Deep Learning Models

Module 4

Module 5: Natural Language Processing (NLP) • Understanding Natural Language Processing • Text Preprocessing and Tokenization • Sentiment Analysis • Named Entity Recognition (NER) • Language Models (e.g., GPT-3, BERT)

Module 5

Module 6: Computer Vision • Introduction to Computer Vision • Image Processing Techniques • Object Detection and Image Classification • Applications of Computer Vision

Module 6

Module 7: Ethics and Bias in AI • Addressing Bias in AI • Fairness, Accountability, and Transparency in AI (FAT/ML) • Ethical AI Development Practices • Case Studies on AI Ethical Issues

Module 7

Module 8: AI Tools and Frameworks • Overview of AI Development Tools (e.g., TensorFlow, PyTorch) • Hands-on Introduction to AI Frameworks • Setting Up AI Development Environment

Module 8

Module 9: AI Applications and Industries • AI in Healthcare • AI in Finance • AI in Autonomous Vehicles • AI in Robotics • Emerging AI Trends

Module 9

Module 10: Capstone Project • Final Project: Building an AI Model or Application • Presentation of Projects • Peer Evaluation and Feedback • Preparing for AI Job Interviews

Module 10

Student Ratings & Reviews

About Us

Internships

Useful Links

Social Media

Intrduction of course
This is Introduction Video of Course

Module 1: Introduction to Artificial Intelligence
• Understanding the Significance of Artificial Intelligence • A Brief History and Evolution of AI • Overview of Various Career Paths in Artificial Intelligence • Tips for Starting a Career in AI

Module 2: Fundamentals of Artificial Intelligence
• Defining Artificial Intelligence • Types of Artificial Intelligence: Narrow vs. General AI • Machine Learning vs. Deep Learning vs. Reinforcement Learning • Ethical Considerations in AI

Module 3: Machine Learning Basics
• Introduction to Machine Learning • Supervised Learning, Unsupervised Learning, and Reinforcement Learning • Key Machine Learning Algorithms (e.g., Linear Regression, Decision Trees, Neural Networks) • Data Preparation and Feature Engineering

Module 4: Deep Learning
• Exploring Deep Learning • Neural Networks and Deep Learning Architectures • Convolutional Neural Networks (CNNs) for Image Analysis • Recurrent Neural Networks (RNNs) for Sequence Data • Training Deep Learning Models

Module 5: Natural Language Processing (NLP)
• Understanding Natural Language Processing • Text Preprocessing and Tokenization • Sentiment Analysis • Named Entity Recognition (NER) • Language Models (e.g., GPT-3, BERT)

Module 6: Computer Vision
• Introduction to Computer Vision • Image Processing Techniques • Object Detection and Image Classification • Applications of Computer Vision

Module 7: Ethics and Bias in AI
• Addressing Bias in AI • Fairness, Accountability, and Transparency in AI (FAT/ML) • Ethical AI Development Practices • Case Studies on AI Ethical Issues

Module 8: AI Tools and Frameworks
• Overview of AI Development Tools (e.g., TensorFlow, PyTorch) • Hands-on Introduction to AI Frameworks • Setting Up AI Development Environment

Module 9: AI Applications and Industries
• AI in Healthcare • AI in Finance • AI in Autonomous Vehicles • AI in Robotics • Emerging AI Trends

Module 10: Capstone Project
• Final Project: Building an AI Model or Application • Presentation of Projects • Peer Evaluation and Feedback • Preparing for AI Job Interviews