INTRODUCTION TO ARTIFICIAL INTELLIGENCE
- Defining AI and its historical context
- Types of AI: Narrow vs. General AI
- Impact of AI on society and industries.
MACHINE LEARNING FUNDAMENTALS
- Basics of machine learning: Supervised, unsupervised, reinforcement learning
- Training and testing data
- Regression and classification algorithms.
NEURAL NETWORKS AND DEEP LEARNING
- Understanding neural networks and their architecture
- Deep learning applications: Image recognition, language translation
- Introduction to popular frameworks (e.g., TensorFlow, PyTorch).
NATURAL LANGUAGE PROCESSING (NLP)
- NLP overview and challenges
- Text preprocessing and tokenization
- Sentiment analysis and language generation
AI ETHICS AND CONSIDERATIONS
- Ethical implications of AI: Bias, privacy, job displacement
- AI regulations and guidelines
AI IN PRACTICE
- Case studies across industries: Healthcare, finance, manufacturing, etc.
- Implementing a simple AI project: From idea to execution
FUTURE TRENDS AND BEYOND
- Emerging trends in AI: Explainable AI, AI for social good, etc.
- AI’s role in shaping the future.
INTRODUCTION TO ROBOTIC APPLICATIONS AND AI INTEGRATION
BASICS OF ROBOTIC APPLICATIONS
- Overview of robotics in diverse industries
- Key roles of robotics in streamlining operations.
ROBOTICS AND AI SYNERGY
- Understanding the connection between robotics and artificial intelligence
- Role of AI in enhancing robotic capabilities.
ROBOTIC PROCESS AUTOMATION
- Introduction to RPA and its relevance in business processes
- Use cases: Data entry, customer support, workflow automation.
CASE STUDIES: AI-DRIVEN ROBOTIC SOLUTIONS
- Real-world examples showcasing AI-integrated robotics.
- Analysis of benefits and efficiencies achieved through AI-robotics synergy.
