Artificial Intelligence & Machine Learning

  • Powering Innovation Through AI & ML
Course Overview

To transform learners from AI-aware to AI-capable, ensuring they can build, apply, evaluate, and deploy AI solutions, not just complete modules.

Full Training Course Contents
LEVEL 1: AI Foundations (AI Literacy & Thinking)
What AI is
  • Definition of AI
  • Difference between AI, ML, Deep Learning, Automation
  • Narrow AI vs General AI vs Super AI
  • Real-world AI use cases
Why AI matters
  • AI in business decision-making
  • AI in productivity & automation
  • AI’s impact on jobs and skills
Skill Mastery Indicators
  • Explain AI concepts in simple language
  • Identify AI usage in daily life
  • Differentiate hype vs reality
Core Concepts
  • Structured, Unstructured & Semi-structured data
  • Data lifecycle
  • Data bias and data quality
Hands-on
  • Understanding datasets (CSV, Excel)
  • Basic data exploration
Skill Mastery Indicators
  • Assess data quality
  • Explain “bad data = bad AI”
LEVEL 2: Machine Learning (Core AI Engine)
Concepts
  • What is Machine Learning?
  • Supervised vs Unsupervised vs Reinforcement Learning
  • Features, labels, training, testing
Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • KNN
Skill Mastery Indicators
  • Can choose the right ML approach for a problem
  • Can explain model behavior and outcomes
Key topics
  • Overfitting & Underfitting
  • Accuracy, Precision, Recall, F1-score
  • Confusion Matrix
  • Bias-Variance Tradeoff
Hands-on
  • Evaluating model performance
  • Improving model accuracy
Skill Mastery Indicators
  • Can interpret model metrics
  • Can justify model improvements
LEVEL 3: Deep Learning & Neural Networks
Core concepts
  • Neurons, weights, bias, activation functions
  • Forward & backward propagation
  • Loss functions
Frameworks
  • TensorFlow
  • PyTorch (intro)
Skill Mastery Indicators
  • Can explain how neural networks “learn”
  • Can build a simple neural network
Topics
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • LSTM & GRU
  • Transformers (high-level intro)
Applications
  • Image recognition
  • Speech processing
  • Time-series prediction
Skill Mastery Indicators
  • Can select the correct architecture
  • Can apply DL to real-world problems
LEVEL 4: Generative AI & Large Language Models
Concepts
  • What is Generative AI?
  • Difference between predictive AI & generative AI
  • Foundation models
Tools
  • ChatGPT
  • Gemini
  • Claude
  • Stable Diffusion
Skill Mastery Indicators
  • Can use GenAI responsibly
  • Can design effective prompts
Topics
  • Prompt structure
  • Role prompting
  • Chain-of-thought prompting
  • AI hallucinations & limitations
Hands-on
  • Creating AI assistants
  • Automating workflows with AI
Skill Mastery Indicators
  • Can get consistent, high-quality AI outputs
  • Can design AI-driven workflows
LEVEL 5: Applied AI (Industry Use Cases)
Use cases
  • AI in HR (resume screening, attrition prediction)
  • AI in Finance (fraud detection, forecasting)
  • AI in Marketing (personalization, lead scoring)
  • AI in Operations (predictive maintenance)
Skill Mastery Indicators
  • Can map AI solutions to business problems
  • Can calculate AI ROI
Topics
  • AI bias & fairness
  • Explainable AI (XAI)
  • Data privacy & compliance
  • AI regulations (India & global overview)
Skill Mastery Indicators
  • Can identify ethical risks
  • Can design responsible AI systems
LEVEL 6: Deployment, MLOps & Real-World Execution
Concepts
  • Model deployment
  • APIs & integration
  • Model monitoring
  • Retraining pipelines
Tools
  • Cloud platforms (AWS, Azure, GCP – overview)
  • CI/CD for ML
Skill Mastery Indicators
  • Can deploy a model
  • Can monitor model drift
Project Types
  • AI-powered chatbot
  • Prediction system
  • Recommendation engine
  • AI dashboard for business insights
Evaluation
  • Problem understanding
  • Model design
  • Business impact
  • Explainability
Skill Mastery Indicators
  • Can independently build & explain an AI solution
  • Can defend design choices
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