Artificial Intelligence Training

Master AI concepts including search, reasoning, NLP, and computer vision with hands-on projects. Build intelligent systems that can learn and adapt.

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AI Training
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AI Curriculum

  • Introduction to Python & Installation (Jupyter, VS Code, Anaconda)
  • Variables, Data Types, Loops, Conditionals, and Functions
  • File Handling, Exception Handling & Logging
  • Object-Oriented Programming (OOP): Classes, Inheritance, and Polymorphism
  • NumPy for Numerical Computing & Matrix Operations
  • Pandas for Data Manipulation, Cleaning & Exploration
  • Matplotlib & Seaborn for Data Visualization
  • Project: Building an AI-Ready Data Processing Pipeline in Python
  • History of AI: From Turing Machines to Modern Deep Learning
  • AI Agent Architecture: Sensors, Effectors, and Environment Interaction
  • Problem Solving via State Space Search: BFS, DFS, and Uniform Cost Search
  • Informed Search Algorithms: A* Search, Greedy Best-First, and Hill Climbing
  • Game Playing AI: Minimax Algorithm with Alpha-Beta Pruning
  • Constraint Satisfaction Problems (CSP): Backtracking and Arc Consistency
  • Knowledge Representation: Propositional Logic, First-Order Logic & Semantic Networks
  • Project: Building a Maze-Solving AI Agent using A* Search Algorithm
  • Introduction to Machine Learning: Supervised, Unsupervised & Reinforcement Learning
  • House Price Prediction using Linear Regression with Scikit-learn
  • Customer Churn Prediction using Logistic Regression
  • Handwritten Digit Recognition using Support Vector Machines (SVM)
  • Spam Email Classification using Naïve Bayes Classifier
  • Decision Trees & Random Forests for Classification & Regression
  • K-Means Clustering for Customer Segmentation
  • Model Evaluation: Confusion Matrix, Precision, Recall, F1-Score & ROC Curves
  • Project: End-to-End ML Pipeline for Predicting Student Performance
  • Perceptrons & Multi-Layer Neural Networks from Scratch
  • Backpropagation Algorithm: Mathematical Derivation & Implementation
  • Activation Functions: ReLU, Sigmoid, Softmax, and Swish
  • Optimizers: SGD, Adam, RMSprop & Learning Rate Scheduling
  • Regularization Techniques: Dropout, Batch Normalization & Early Stopping
  • Building Neural Networks with PyTorch: Tensors, Autograd & DataLoaders
  • Building Neural Networks with TensorFlow/Keras: Sequential & Functional API
  • Hyperparameter Tuning using Grid Search, Random Search & Optuna
  • Project: Image Classification on CIFAR-10 Dataset using PyTorch
  • Text Preprocessing: Tokenization, Stemming, Lemmatization & Stop-word Removal
  • Text Representation: Bag of Words, TF-IDF & Word2Vec Embeddings
  • Sentiment Analysis using LSTM (Long Short-Term Memory) Networks
  • Sequence-to-Sequence Models for Machine Translation
  • The Transformer Architecture: Self-Attention & Multi-Head Attention Deep Dive
  • BERT for Text Classification and Named Entity Recognition (NER)
  • GPT & Large Language Models: Fine-tuning with LoRA and QLoRA
  • Prompt Engineering: Zero-shot, Few-shot & Chain-of-Thought Prompting
  • Project: Building a Sentiment Analysis API using BERT and FastAPI
  • Image Processing: Convolutions, Edge Detection, and Spatial Filtering with OpenCV
  • Convolutional Neural Networks (CNNs): LeNet, AlexNet, VGG & ResNet Architectures
  • Transfer Learning: Using Pre-trained Models for Custom Image Classification
  • Object Detection with YOLO (You Only Look Once) v8
  • Image Segmentation: U-Net for Medical Image Segmentation
  • Face Detection & Recognition using OpenCV and Deep Learning
  • Generative AI for Images: GANs (Generative Adversarial Networks) & Diffusion Models
  • Project: Real-time Object Detection System using YOLOv8 with Webcam Feed
  • Markov Decision Processes (MDP): States, Actions, Rewards & Transitions
  • Q-Learning: Tabular Reinforcement Learning from Scratch
  • Deep Q-Networks (DQN) for Playing Atari Games
  • Policy Gradient Methods: REINFORCE & Actor-Critic Algorithms
  • Proximal Policy Optimization (PPO) for Continuous Control Tasks
  • OpenAI Gymnasium: Training RL Agents in Simulated Environments
  • Multi-Agent Systems and Cooperative AI
  • Project: Training an AI Agent to Play CartPole using DQN
  • Algorithmic Bias: Detecting & Mitigating Unfairness in AI Systems
  • Explainable AI (XAI): Model Interpretability using SHAP and LIME
  • AI Safety & Alignment: Ensuring AI Behaviors Match Human Values
  • Model Deployment: Serving AI Models with Flask, FastAPI & Docker
  • Cloud AI Services: AWS SageMaker, Azure ML Studio & Google Vertex AI
  • MLOps Basics: Model Versioning, Experiment Tracking with MLflow
  • AI Legal Frameworks: GDPR, EU AI Act & Responsible AI Guidelines
  • Project: Deploying a Full AI Application to Cloud with CI/CD Pipeline