Artificial Intelligence Course - Best AI Course by Prerogative

• Introduction to artificial neural networks
• Building a simple feedforward neural network
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Deep Learning Frameworks (TensorFlow, PyTorch)
• Regularization and dropout
• Natural Language Processing (NLP) with Deep Learning

• Text Preprocessing and Tokenization
• Text Classification and Sentiment Analysis
• Named Entity Recognition and Part-of-Speech Tagging
• Word embeddings (Word2Vec, GloVe)
• Sequence-to-Sequence Models and Machine Translation
• Transformers and BERT

• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Q-Learning and Value Iteration
• Deep Q Networks (DQNs)
• Policy Gradients and Actor-Critic Methods
• Applications of Reinforcement Learning

• Bias and fairness in AI
• Privacy and Security in AI
• AI Regulation and Policy
• Ethical considerations in AI development

• Generative Adversarial Networks (GANs)
• Transformers for NLP
• Reinforcement learning algorithms (e.g., A3C, PPO)

• Introduction to Robotics and Robot Control
• Simultaneous Localization and Mapping (SLAM)
• Robot kinematics and dynamics
• Simultaneous Localization and Mapping (SLAM)
• Autonomous navigation
• Reinforcement Learning for Robotics

• Medical image analysis
• Predictive modeling for disease diagnosis
• Time series forecasting
• Customer churn prediction
• Recommender systems

• Current AI Research Areas
• Staying updated with the latest AI research
• Future Trends in AI
• Preparing for a Career in AI Research
• Participating in AI competitions (e.g., Kaggle)

Students work on a real-world AI project from problem formulation to deployment.