Course Modules
Artificial Intelligence (AI) and machine learning most times are used interchanged, though machine learning is a subset of AI. Artificial intelligence is the ability of a computer to emulate human thoughts and perform tasks in real-world environments while machine learning is the technologies and algorithms that enable systems to identify patterns, make decisions and improve themselves through experience and data.
Artificial Intelligence (AI) is a broad area of computer science focused on creating systems that can perform tasks that typically require human intelligence. These tasks can include reasoning, problem-solving, understanding natural language, recognizing patterns, and more.
 Machine Learning (ML) is a subset of AI that involves the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Instead of explicitly programming rules, machine learning systems use data to improve their performance.
 That is to say, AI is the overarching concept, while machine learning is a specific technique within AI that allows machines to learn and improve from data. Machine learning is used in various AI applications, such as natural language processing, image recognition, recommendation systems, and more.
Course Modules
- Introduction to Machine Learning:
  – Basics of machine learning
  – Types of machine learning (supervised, unsupervised, reinforcement, etc.)
- Data Preprocessing:
  – Data cleaning and transformation
  – Feature engineering
- Supervised Learning:
  – Linear regression
  – Logistic regression
  – Decision trees
  – Random forests
  – Support vector machines
  – Neural networks
- Unsupervised Learning:
  – Clustering algorithms (K-means, hierarchical, DBSCAN)
  – Dimensionality reduction (PCA, t-SNE)
- Evaluation and Model Selection:
  – Cross-validation
  – Model metrics (accuracy, precision, recall, F1-score, etc.)
- Deep Learning:
  – Introduction to neural networks
  – Convolutional neural networks (CNNs)
  – Recurrent neural networks (RNNs)
  – Transfer learning
- Natural Language Processing (NLP):
  – Text preprocessing
  – Word embeddings (Word2Vec, GloVe)
  – Sentiment analysis
  – Sequence-to-sequence models
- Reinforcement Learning:
  – Markov Decision Processes (MDPs)
  – Q-learning
  – Deep Q Networks (DQN)
- Specialized Topics:
  – Time series analysis
  – Recommender systems
  – Generative Adversarial Networks (GANs)
  – Explainable AI (XAI)
- Capstone Project:
   – Applying machine learning to a real-world problem
- Ethical and Responsible AI:
   – Bias and fairness in AI
   – Data privacy and security
- Emerging Trends:
   – Edge AI, Federated Learning, etc.
