Artificial Intelligence & Machine Learning

Artificial Intelligence & Machine Learning: Certification Programme

  • Build a solid understanding of AI, machine learning (ML), and data fundamentals.
  • Introduction to Artificial Intelligence
    • • History and Evolution of AI
    • • Types of AI: Narrow, General, and Super AI
    • • AI in Industry: Use Cases (Healthcare, Finance, Manufacturing, etc.)
  • Mathematics for AI
    • • Linear Algebra basics
    • • Probability & Statistics
    • • Calculus essentials (derivatives, gradients)
  • Programming for AI
    • • Python Basics
    • • NumPy, Pandas, Matplotlib, Scikit-learn intro
  • Introduction to Machine Learning
    • • Supervised vs Unsupervised Learning
    • • Key Algorithms: Linear Regression, KNN, Decision Trees
    • • Model evaluation (accuracy, confusion matrix)
  • Ethics and Risks in AI
    • • AI Bias & Fairness
    • • Privacy Concerns
    • • Real-world implications
  • Build a linear regression model on house pricing data
  • Classification on Iris dataset
  • Apply ML concepts in depth and introduce neural networks and data engineering.
  • Data Pre-processing & Feature Engineering
    • • Data Cleaning, Missing Data
    • • Feature Scaling, Encoding, Selection
  • Supervised Learning – Advanced
    • • SVM, Random Forest, Gradient Boosting (XGBoost, LightGBM)
    • • Hyperparameter Tuning (Grid Search, Randomized Search)
  • Unsupervised Learning
    • •K-Means Clustering, DBSCAN, PCA
    • • Use in anomaly detection, recommendation
  • Introduction to Deep Learning
    • • Neural Networks Basics
    • • Activation Functions, Backpropagation
    • • TensorFlow / Keras or PyTorch Introduction
  • Model Deployment
    • • Flask / Fast API Basics
    • • Introduction to Docker
    • • Deploying AI models to the cloud (AWS/GCP/Azure overview)
  • • Credit Card Fraud Detection
  • • Customer Segmentation with K-Means
  • • Build and deploy an image classifier web app
  • Master deep learning, apply AI in real-world domains, and focus on scalability and performance.
  • Advanced Deep Learning
    • • CNNs (Image Classification, Object Detection)
    • • RNNs, LSTMs, GRUs
    • • Transfer Learning (ResNet, VGG)
  • Natural Language Processing (NLP)
    • • Text Pre-processing, Word Embeddings (Word2Vec, GloVe)
    • • Sentiment Analysis, Named Entity Recognition
    • • Transformers & BERT (Hugging Face intro)
  • Computer Vision
    • • Image Augmentation, OpenCV Basics
    • • YOLO, SSD, and real-time detection
  • AI for Edge and Mobile
    • • TensorFlow Lite / ONNX
    • • AI on Raspberry Pi / Jetson Nano
  • MLOps & Scalability
    • • ML Pipelines (MLFlow, TFX)
    • • Model Monitoring, Retraining
    • • Versioning, CI/CD for ML
  • • Deploy a chatbot using BERT
  • • Real-time object detection on webcam
  • • End-to-end MLOps pipeline on a cloud platform
  • • Weekly Lectures + Practical Labs
  • • Capstone Projects at each level
  • • Hackathons/Challenges
  • • Online LMS or GitHub Classroom
  • • Industry Expert Sessions
  • Issue digital certificates for each level
  • Capstone project evaluation by peers or mentors