AI Engineering focuses on building and deploying AI systems at scale. This course is designed for those who want to specialize in designing, implementing, and maintaining AI-powered solutions.
● Descriptive and Inferential Statistics
● Probability Distributions
● Bayesian Inference
● Hypothesis Testing
● Statistical Significance
● Linear Programming
● Convex Optimization
● Genetic Algorithms
● Swarm Intelligence
● Markov Decision Processes (MDP)
● Policy-Based and Value-Based Methods
● Q-Learning and Deep Q-Networks (DQN)
● Actor-Critic Methods
● Multi-Agent Reinforcement Learning
● Image Processing Techniques (Filtering, Edge Detection)
● Object Tracking and Motion Analysis
● 3D Computer Vision
● Optical Flow and Video Analysis
● Augmented Reality and Virtual Reality
■ Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)
■ Word Embeddings (Word2Vec, GloVe, FastText)
■ Sequence Models for NLP (RNN, LSTM, Transformers)
■ Sentiment Analysis and Text Classification
■ Named Entity Recognition (NER) and Machine Translation
■ Introduction to Generative Models
■ GAN Architecture (Generator and Discriminator)
■ Training Process and Loss Functions
■ Variants of GANs (DCGAN, CycleGAN, StyleGAN)
■ Applications of GANs (Image Generation, Super-Resolution, Deepfake)
■ Image Classification and Object Detection
■ Face Recognition Systems
■ Autonomous Vehicles and Self-Driving Cars
■ AI-Powered Chatbots and Virtual Assistants
■ Fraud Detection and Anomaly Detection
■ Medical Image Analysis and Healthcare AI
