Artificial Intelligence and Machine Learning

Course Overview

This Foundation-to-Intermediate certification program provides learners with a comprehensive understanding of Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Natural Language Processing, Large Language Models (LLMs), Generative AI, AI Agents, and real-world AI applications. The course is designed to build strong conceptual foundations while preparing learners for advanced AI studies, research, innovation, and industry opportunities.

Course Structure

Unit 1: Foundations of Artificial Intelligence and Intelligent Systems

Topics Covered

• Definition and Scope of Artificial Intelligence

• Brief Overview of AI Evolution

• AI in Everyday Life

• AI vs Human Intelligence

• Types of Artificial Intelligence

  • Narrow AI

  • General AI

  • Super AI

• How Artificial Intelligence Works

• Data, Information, Knowledge and Intelligence

• Pattern Recognition

• Learning from Data

• Decision Making Systems

• Prediction-Based Intelligence

• Components of Artificial Intelligence

  • Machine Learning

  • Deep Learning

  • Neural Networks

  • Computer Vision

  • Natural Language Processing

  • Robotics

  • Expert Systems

  • Intelligent Agents

• AI Development Lifecycle

  • Problem Identification

  • Data Collection

  • Model Training

  • Model Evaluation

  • Deployment and Improvement

• Introduction to Generative AI

• AI Assistants

• Large Language Models (LLMs)

Unit 2: Machine Learning and Data Science

Topics Covered

• Fundamentals of Machine Learning

• AI vs Machine Learning vs Deep Learning

• Machine Learning Workflow

• Data Science Foundations

• Data Collection and Preparation

• Data Cleaning and Preprocessing

• Feature Engineering

• Data Visualization

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Linear Regression

• Logistic Regression

• K-Nearest Neighbors (KNN)

• Decision Trees

• Random Forest

• K-Means Clustering

• Model Training

• Validation Techniques

• Testing Dataset

• Accuracy, Precision, Recall and F1 Score

Unit 3: Neural Networks and Deep Learning

Topics Covered

• Biological Neurons and Artificial Neurons

• Perceptron Model

• Neural Network Architecture

• Input Layer, Hidden Layer and Output Layer

• Weights and Biases

• Activation Functions

  • Sigmoid

  • Tanh

  • ReLU

  • Softmax

• Forward Propagation

• Backpropagation

• Loss Functions

• Model Optimization

• Convolutional Neural Networks (CNN)

• Image Classification

• Object Detection

• Face Recognition

• Recurrent Neural Networks (RNN)

• Sequential Data Processing

• Long Short-Term Memory (LSTM)

• Transformer Architecture

• Modern Deep Learning Systems

Unit 4: Natural Language Processing, LLMs and Conversational AI

Topics Covered

• Introduction to Natural Language Processing

• Text Processing Fundamentals

• Tokenization

• Stop Word Removal

• Stemming

• Lemmatization

• Text Classification

• Sentiment Analysis

• Named Entity Recognition

• Machine Translation

• Text Summarization

• Conversational AI

• Chatbots

• Virtual Assistants

• Question Answering Systems

• Large Language Models (LLMs)

• GPT Models

• BERT

• LLaMA

• Gemini

• Prompt Engineering

• Generative AI Applications

• AI Agents

• Autonomous Task Execution

Unit 5: AI Applications, Industry Projects and Professional Development

Topics Covered

AI Applications

• AI in Healthcare

  • Medical Imaging

  • Disease Prediction

  • Personalized Medicine

  • Drug Discovery

• AI in Finance

  • Fraud Detection

  • Financial Forecasting

  • Risk Analysis

• AI in Engineering and Manufacturing

  • Robotics

  • Automation

  • Predictive Maintenance

  • Quality Assurance

• AI in Agriculture

  • Smart Farming

  • Crop Monitoring

  • Precision Agriculture

• AI in Education

  • Personalized Learning

  • Intelligent Tutoring Systems

Responsible AI

• Fairness and Bias

• Privacy and Security

• Explainable AI

• Responsible AI Development

Industry-Oriented Projects

• AI Chatbot

• Face Recognition System

• Student Performance Predictor

• Smart Attendance System

• Disease Prediction System

• Recommendation Engine

• AI Content Generator

Career Development

• Portfolio Building

• Project Documentation

• Research Opportunities

• AI Career Pathways

Learning Outcomes

Upon successful completion of this course, learners will be able to:

• Understand the complete Artificial Intelligence ecosystem.

• Apply Machine Learning concepts to real-world problems.

• Understand Neural Networks and Deep Learning architectures.

• Analyze CNN, RNN, LSTM and Transformer models.

• Understand Natural Language Processing and Large Language Models.

• Explore Generative AI and AI Agents.

• Develop AI-based projects and portfolio work.

• Prepare for advanced AI specialization programs and industry opportunities.