Introduction to Artificial Intelligence
Programming Crash Course
- Python: Basics, Loops, Blocks and Statements, Lists, Sets, File I/O, Modules and Functions, OOP Concepts
- Keras: Introduction, Installation, and Configuration
- TensorFlow: Tensors, TensorFlow Installation, TensorFlow Basics
Data Analytics
- Big Data Analytics: Expand coverage to include big data analytics concepts and technologies such as Apache Hadoop, Apache Spark, and distributed computing frameworks for processing large-scale datasets.
Mathematics for AI
- Probability Theory and Statistics: Expand coverage of probability theory and statistical methods relevant to AI, including Bayesian inference, statistical hypothesis testing, and probabilistic graphical models.
Deep Learning
- Introduction to Neural Networks
- Deep Neural Networks
- Perceptrons
- RNN (Recurrent Neural Networks)
- CNN (Convolutional Neural Networks)
- LSTM (Long Short-Term Memory)
- Deep Belief Network
- Semantic Hashing
- Building Deep Learning Models/Neural Networks using Keras and TensorFlow
- Testing and Training the Models
Convolutional Neural Networks
Natural Language Processing & Computer Vision
- Multimodal Learning: Explore methods for combining text, image, and other modalities in AI applications, such as multimodal embeddings, fusion architectures, and cross-modal retrieval.
- Ethical Considerations in AI Applications
AI Compute Platforms, Applications & Trends
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