Artificial Intelligence
Detailed Course Syllabus
Program Overview
Learn to build intelligent systems. Understand foundational AI concepts, neural networks, and how to integrate large language models (LLMs) into production applications.
Target Audience
Software engineers and tech professionals looking to pivot into AI.
Tools & Technology
Python, TensorFlow, PyTorch, OpenAI API
Learning Outcomes
Design, train, and deploy AI models for real-world business problems.
Project Work
Build a scalable Knowledge Assistant using Retrieval-Augmented Generation (RAG).
Curriculum Breakdown
1Module 1: Foundations of Artificial Intelligence
- •Introduction to AI, History, and Future Trends
- •Search Algorithms and Heuristics
- •Knowledge Representation and Reasoning
- •Fuzzy Logic and Expert Systems
- •Ethics in AI and Bias Mitigation
2Module 2: Deep Learning & Neural Networks
- •Artificial Neural Networks (ANN) Architecture
- •Activation Functions, Backpropagation & Gradient Descent
- •Convolutional Neural Networks (CNN) for Image Processing
- •Recurrent Neural Networks (RNN) and LSTMs
- •Implementing Models using TensorFlow and PyTorch
3Module 3: Natural Language Processing (NLP)
- •Text Preprocessing: Tokenization, Stemming, Lemmatization
- •Word Embeddings (Word2Vec, GloVe)
- •Transformers Architecture (Attention is All You Need)
- •Fine-Tuning Pre-trained Models (Hugging Face)
- •Building Chatbots and Sentiment Analysis Systems
4Module 4: Generative AI & MLOps
- •Understanding Large Language Models (LLMs) like GPT-4
- •Prompt Engineering Techniques
- •Retrieval-Augmented Generation (RAG) Systems
- •Deploying AI Models with Docker and Kubernetes (MLOps)
- •Monitoring and Scaling AI in Production
This syllabus is proprietary to Misoftware Solutions. Content may be updated to reflect current industry standards.