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Course Outline
Introduction to Artificial Intelligence
- The evolution of AI
- Key definitions and industry terminology
- Comparing AI with human intelligence
- Emerging trends and future potential
Foundations of Machine Learning
- Machine learning paradigms: supervised, unsupervised, and reinforcement learning
- Essential ML algorithms
- The ML lifecycle: from gathering data to evaluating models
Data Stewardship
- Techniques for data acquisition
- Cleaning and preprocessing data
- Analyzing and visualizing data
Practical AI Applications
- Case studies of real-world AI implementations
- AI solutions tailored to specific industries
- The role of AI in consumer-facing products
Ethical Frameworks
- The impact of AI on employment
- Addressing bias and ensuring fairness
- Challenges related to privacy and security
- The future landscape of AI ethics
Practical Lab Work
- Programming tasks using Python
- Data analysis projects utilizing real-world datasets
- Building a basic ML model
Wrap-up and Path Forward
Requirements
- A grasp of fundamental programming principles
- Proficiency in Python programming
- Knowledge of basic statistical and mathematical concepts
Target Audience
- IT Professionals
14 Hours
Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.