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Exploring Artificial Intelligence: Concepts, Techniques, and Applications

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DotFiv Team

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Course Overview

Artificial Intelligence (AI) is a transformative field that encompasses a wide range of technologies and applications aimed at simulating human intelligence in machines. In this comprehensive course, participants will explore the foundational concepts, techniques, and applications of AI, ranging from machine learning and natural language processing to computer vision and robotics. Through a combination of lectures, hands-on projects, and real-world case studies, students will gain a deep understanding of AI principles and practical skills to develop AI-powered solutions. Whether you are a beginner curious about the potential of AI or an experienced professional looking to advance your expertise, this course will provide you with the knowledge and tools to harness the power of artificial intelligence in various domains.


Course Objectives:

  • Understand the foundational concepts and history of artificial intelligence.
  • Explore various subfields of AI, including machine learning, natural language processing,
    computer vision, and robotics.
  • Gain practical experience in implementing AI algorithms and techniques through hands-on
    projects.
  • Examine real-world applications of AI across different industries and domains.
  • Develop critical thinking skills to evaluate ethical and societal implications of AI technologies.

Course Content

  • Introduction to Artificial Intelligence

    • Overview of artificial intelligence and its history

    • Understanding the Turing Test and the goals of AI

    • Exploring the impact of AI on society and industries

  • Machine Learning Fundamentals

    • Introduction to machine learning and its types (supervised, unsupervised, reinforcement learning)

    • Understanding key machine learning concepts: features, labels, training, testing

    • Exploring popular machine learning algorithms: linear regression, logistic regression, decision trees, and k-nearest neighbors

  • Deep Learning and Neural Networks

    • Introduction to deep learning and neural networks

    • Understanding the architecture of artificial neural networks (ANNs)

    • Exploring deep learning frameworks (TensorFlow, PyTorch) and libraries for building neural networks

    • Implementing neural networks for image classification, natural language processing, and other tasks

  • Natural Language Processing (NLP)

    • Introduction to natural language processing and its applications

    • Understanding text preprocessing techniques: tokenization, stemming, lemmatization

    • Exploring NLP tasks: sentiment analysis, named entity recognition, text generation

    • Building NLP models using frameworks like NLTK (Natural Language Toolkit) and spaCy

  • Computer Vision

    • Introduction to computer vision and its applications

    • Understanding image processing techniques: image enhancement, segmentation, feature extraction

    • Exploring computer vision tasks: object detection, image classification, image segmentation

    • Building computer vision models using deep learning frameworks like OpenCV and TensorFlow

  • Reinforcement Learning

    • Introduction to reinforcement learning and its applications

    • Understanding the reinforcement learning framework: agents, environments, actions, rewards

    • Exploring reinforcement learning algorithms: Q-learning, deep Q-networks (DQN), policy gradients

    • Implementing reinforcement learning agents for tasks like game playing and robotic control

  • Robotics and Autonomous Systems

    • Introduction to robotics and autonomous systems

    • Understanding robot perception, decision-making, and control

    • Exploring robotic applications: autonomous vehicles, drones, industrial robots

    • Implementing robotic systems using frameworks like ROS (Robot Operating System)

  • Ethical and Societal Implications of AI

    • Understanding ethical considerations in AI development and deployment

    • Exploring biases and fairness in AI algorithms

    • Examining privacy and security concerns in AI applications

    • Discussing the role of AI in shaping the future of work and society

  • Real-World Applications of AI

    • Examining case studies and examples of AI applications across industries

    • Exploring AI-powered solutions in healthcare, finance, transportation, and other domains

    • Understanding challenges and opportunities in deploying AI systems in real-world settings

  • Project: Building an AI Application

    • Applying AI knowledge and skills to develop an AI-powered application

    • Identifying a problem domain and defining project objectives

    • Designing and implementing AI algorithms and models

    • Testing, evaluating, and deploying the AI application

₹4,999.00
  • Course Level Beginner
  • Lessons 37
  • Additional Resource 0
  • Last Update April 8, 2024