Advance AI (Artificial Intelligence)
Duration:
6 Months
About Advance AI
The Advanced AI course provides in-depth knowledge of cutting-edge AI techniques, including deep learning, reinforcement learning, natural language processing, and computer vision. Students explore advanced topics such as generative adversarial networks (GANs), transformer models, and ethical considerations in AI. Hands-on projects and real-world applications empower students to master complex AI concepts and contribute to the forefront of AI research and development.
Course Curriculum:
- Overview of AI and its subfields
- Introduction to data science and its significance
- Ethical considerations in AI and data science
- Review of Python basics
- Libraries for data manipulation and analysis (NumPy, pandas)
- Data visualization (Matplotlib, Seaborn)
- Descriptive and inferential statistics
- Probability distributions
- Hypothesis testing and confidence intervals
- Supervised learning, unsupervised learning, and reinforcement learning
- Feature engineering and preprocessing
- Model evaluation and validation techniques
- Matrices and vectors
- Eigenvalues and eigenvectors
- Gradient descent and optimization
- Handling missing data
- Data normalization and scaling
- Dealing with outliers
- Linear regression
- Logistic regression
- Decision trees and random forests
- Support vector machines
- Clustering algorithms (K-means, hierarchical clustering)
- Dimensionality reduction (PCA, t-SNE)
- Basics of artificial neural networks
- Deep learning frameworks (TensorFlow, PyTorch)
- Convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
- Tokenization and text preprocessing
- Sentiment analysis
- Language models and embeddings
- Handling time-series data
- Seasonality and trend analysis
- Time series forecasting
- Introduction to big data technologies.
- Cloud platforms (AWS, Google Cloud) for data science
- Responsible AI and ethical considerations
- Data privacy regulations (GDPR, CCPA)
- Techniques for selecting and creating relevant features
- Feature importance and dimensionality reduction
- Deploying machine learning models in production
- REST APIs and model monitoring
- Advanced visualization techniques
- Interpreting complex models
- Real-world project applying AI and data science concepts
- Data collection, preprocessing, modeling, and presentation
- Reinforcement learning algorithms
- Generative adversarial networks (GANs)
- Bayesian methods
