🧠Artificial Intelligence

🧠 AI Training Modules

Fundamental → Intermediate → Advanced levels covering machine learning, neural networks, and AI project implementation.

  • 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 Preprocessing & Feature Engineering
    • Data Cleaning, Handling Missing Data
    • Feature Scaling, Encoding, Feature Selection
  • Supervised Learning – Advanced
    • SVM, Random Forest, Gradient Boosting (XGBoost, LightGBM)
    • Hyperparameter Tuning (GridSearch, RandomizedSearch)
  • Unsupervised Learning
    • K-Means Clustering, DBSCAN, PCA
    • Applications in anomaly detection and recommendations
  • Introduction to Deep Learning
    • Neural Networks Basics
    • Activation Functions, Backpropagation
    • Introduction to TensorFlow/Keras or PyTorch
  • Model Deployment
    • Flask and FastAPI Basics
    • Introduction to Docker
    • Deploying AI Models to 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 Preprocessing, 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