Fundamentals of Artificial Intelligence (AI)

Apply Now

Course Description

The Global Institute of Technology ‘Fundamentals of Artificial Intelligence ‘ is a Foundational Certification program designed to prepare learners for entry level employment opportunities in the field of Artificial Intelligence. Throughout this program, students will acquire a solid grounding in fundamental AI principles and gain proficiency in the necessary tools to confidently step into the realm of emerging AI opportunities. Our curriculum is meticulously designed to empower learners with the knowledge that is pivotal in shaping the digital landscape of today and tomorrow. During the training, you will be immersed in a comprehensive exploration of core concepts, methodologies, and practical applications that are intrinsic to the AI and ML domain.


Our course is tailored to pave the way for individuals to stride into the field of technology and innovation’s future, armed with the exceptional capabilities of AI and Machine Learning. By the end of this program, you’ll be poised to embrace the ever-evolving world of AI with confidence and competence.

Course Objectives

Upon completing this comprehensive course in Fundamentals of Artificial Intelligence (AI), participants will:

  • Develop Foundational Understanding
  • Understand AI’s Practicality
  • Grasp Machine Learning Essentials
  • Harness Machine Learning Tools
  • Uncover Neural Network Dynamics
  • Navigate Advanced AI Applications
  • Evaluate Ethical Implications
  • Examine AI in Diverse Sectors:
  • Explore Emerging AI Frontiers
  • Work on a Hands-on Capstone Project

 

By the end of this course, participants will have gained a comprehensive understanding of Fundamentals of Artificial Intelligence (AI), practical skills, and the ability to apply them to real-world scenarios, fostering their capability to excel in the dynamic field of artificial intelligence.

Prerequisites

The student must have a High School Diploma or GED equivalent. Basic Python Programming and Data Analysis skills are recommended.

Course Duration

80 Hours (40 Hours Instructor-led live training and 40 Hours Instructor Guided).

Prerequisites

The student must have a High School Diploma or GED equivalent. Basic Python Programming and Data Analysis skills are recommended.

Course Duration

80 Hours (40 Hours Instructor-led live training and 40 Hours Instructor Guided).

Course Contents

  • Definitions and importance of AI
  • Historical milestones in AI
  • Major AI achievements and setbacks
  • Turing Test and its relevance
  • Lab: AI is everywhere! Prediction, cognition and optimization. Opacity and complexity of AI. Importance of Explainability.
  • Introduction to statistics and machine learning
  • Supervised, unsupervised, and reinforcement learning 
  • Basic methods: regression, classification, clustering, exploratory data analysis, data visualization, and time series analysis
  • Lab: Data analysis, data visualization, and model training using Amruta Inc XAI software.
  • Introduction to neural networks: perceptrons and multi-layer networks
  • Backpropagation explained
  • Convolutional Neural Networks (CNNs) and applications in computer vision
  • Recurrent Neural Networks (RNNs) and applications in sequence modeling
  • Lab: Image detection.
  • Natural Language Processing: tokenization, embeddings and transformers
  • Reinforcement learning in-depth
  • Robotics: perception, action and control
  • Generative models: GANs, VAEs
  • Lab: Text classification. Sentiment and emotion recognition. Text
    generation/ChatGPT.
  • Bias and fairness in AI
  • Privacy implications: facial recognition and data collection
  • AI in warfare: autonomous weapons
  • The future of work: automation vs. augmentation
  • Lab: Analyzing bias in datasets and mitigation techniques.
  • Healthcare: diagnostics and personalized medicine
  • Finance: fraud detection and robo-advisors
  • Entertainment: game design and movie recommendations
  • Transportation: autonomous vehicles and route optimization
  • Lab: Industry-specific project, e.g., building a fraud detection system.
  • ChatGPT and Generative AI
  • Prompt Engineering
  • Edge AI: processing on devices
  • AI chips: specialized hardware for AI processing
  • Students choose an AI domain to develop a project.
  • Daily check-ins, troubleshooting, and mentoring.
  • Presentations: Each student/group presents their project.
  • Course review and feedback session.
  • Final assessment: A combination of project evaluation, written test, and viva.

Your AI journey begins here. Join us at Git Services, and let’s explore the limitless possibilities of Artificial Intelligence together.

Resources

  1. Books: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig; “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

 

     2. Tools: Amruta Inc AI/ML/explainable AI software.