Overview of AI, its history, and its applications

Key concepts and terminology

Supervised, unsupervised, and reinforcement learning

Common algorithms: linear regression, decision trees, clustering, etc.

Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs)

Training and optimizing deep learning models

Architecture and functioning of LLMs (e.g., GPT-4)

Training and fine-tuning LLMs for specific tasks

Techniques for designing effective prompts

Optimizing AI responses for accuracy and relevance

Text preprocessing, tokenization, and embedding techniques

Applications in sentiment analysis, text summarization, and translation

Data collection, cleaning, and preprocessing

Automated analytics workflows and pipelines

Building and validating predictive models

Techniques for improving model accuracy and performance

Creating insightful visualizations using tools like Tableau and Power BI

Best practices for presenting data insights

Overview of cloud platforms: AWS SageMaker, Google AI Platform, Azure ML

Deploying and managing AI models on the cloud

Introduction to big data frameworks like Hadoop and Spark

Processing and analyzing large datasets

Evaluating model performance using metrics like precision, recall, F1-score

Techniques for model validation and cross-validation

Ethical considerations and challenges in AI development and deployment

Bias, fairness, and transparency in AI systems

Case studies of AI applications in healthcare, finance, marketing, etc.

Exploring domain-specific AI solutions

Hands-on projects that integrate course components

Real-world scenarios and business challenges