Course Structure

The course is divided into 12 modules, each focusing on a specific aspect of AI. Each module includes lectures, practical exercises, projects, and assessments.

  • Definition and history of AI
  • AI vs. Machine Learning vs. Deep Learning
  • Key concepts and terminology
  • Applications of AI in various industries
  • Current trends and future directions
  • Introduction to machine learning
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Key algorithms: linear regression, logistic regression, k-nearest neighbors, decision trees
  • Model evaluation and validation
  • Practical implementation with Python and scikit-learn
  • Ensemble methods: bagging, boosting, random forests
  • Support Vector Machines (SVM)
  • Clustering algorithms: k-means, hierarchical clustering
  • Dimensionality reduction techniques: PCA, t-SNE
  • Hyperparameter tuning and optimization
  • Introduction to neural networks
  • Architecture of neural networks: neurons, layers, activation functions
  • Training neural networks: forward and backward propagation
  • Deep learning frameworks: TensorFlow, Keras, PyTorch
  • Building and training deep neural networks
  • Basics of CNNs
  • Convolutional and pooling layers
  • Popular CNN architectures: LeNet, AlexNet, VGG, ResNet
  • Image classification and object detection
  • Practical implementation of CNNs
  • Introduction to RNNs
  • RNN architectures: vanilla RNNs, LSTMs, GRUs
  • Applications of RNNs: time series prediction, sequence modeling
  • Practical implementation of RNNs
  • Attention mechanisms and transformers
  • Overview of NLP
  • Text preprocessing techniques
  • Word embeddings: Word2Vec, GloVe, FastText
  • NLP models: RNNs, LSTMs, BERT
  • Text classification, sentiment analysis, language translation
  • Introduction to computer vision
  • Image preprocessing techniques
  • Object detection and segmentation
  • Image generation with GANs
  • Practical applications and projects
  • Basics of reinforcement learning
  • Key concepts: agents, states, actions, rewards
  • Popular algorithms: Q-learning, Deep Q-Networks (DQN), policy gradients
  • Applications of reinforcement learning
  • Practical implementation of reinforcement learning
  • Ethical considerations in AI
  • Bias and fairness in AI algorithms
  • Privacy and data protection
  • AI in society: impact on jobs, economy, and daily life
  • Responsible AI development and deployment
  • AI project lifecycle: problem definition, data collection, model development, deployment
  • AI tools and platforms
  • Case studies of AI applications in various industries
  • Best practices for implementing AI solutions
  • Planning and designing an AI project
  • Data collection and preprocessing
  • Model development and evaluation
  • Deployment and presentation of the AI solution
  • Final project review and feedback
Assessment and Certification
  • Weekly quizzes and assignments
  • Mid-term project: Develop a machine learning model for a specific problem
  • Final capstone project: Create a comprehensive AI solution
  • Participation in class discussions and practical exercises
  • Certification awarded upon successful completion of the course
Prerequisites
  • Basic understanding of programming (preferably Python)
  • Familiarity with basic mathematics and statistics