Star AI Essentials

AI is being incorporated in almost all industries and in our day-to-day lives. AI technology will be virtually pervasive in every new software product and service. This immense opportunity is creating a huge talent shortage. The need for AI professionals exists in industries like healthcare, finance, social media optimization, marketing, transportation, research work, and even coding/programming.

AI intersects with various fields in Day-to-day tasks. The learner acquires interdisciplinary skills that can be applied in diverse domains. This versatility allows the learner to explore different industries and gears up to make an impact in areas that align with their interests.

The Star AI Essentials certification equips you with sought-after skills, enhances your problem-solving abilities, opens up diverse opportunities, and enables you to contribute to innovation and efficiency improvements in various industries. It can provide a fulfilling and impactful career path in a field that is rapidly evolving and shaping our world.

Audience:

Beginners in either IT or non-IT domains who possess limited or no prior knowledge of AI may benefit from this. Familiarity with IT Essentials, mathematics, and statistics can be an additional asset.

Course Objectives:

In this course, you will learn about:

• Essentials of AI
• Role of Data, Algorithm, and Hardware in AI
• Machine learning and Deep learning essentials
• Neural Networks
• Computer vision, voice, and NLP
• AI Tools, Framework and AI applications
• Deployment & ethical considerations
• Emerging Trends in AI

Course Outcomes:

After completing this course, you will be able to recognize and/or demonstrate:

• Explain Artificial Intelligence
• Understand Data and Algorithm in AI
• Understand Hardware in AI
• Interpret Machine and Deep learning
• Identify the application areas of AI
• Recognize the use of AI in emerging technologies
• Use AI Tools
• Working on AI applications

Course Outline

1. Introduction to Artificial Intelligence
2. Branches of AI
3. Role of Data, and Algorithm in AI
4. Role of Hardware in AI
5. Machine Learning Fundamentals
6. Neural Networks
7. Machine Learning in AI
8. Deep Learning in AI
9. Natural Language Processing (NLP)
10. Computer Vision with AI
11. Voice, and NLP with AI
12. Reinforcement Learning
13. Ethics and Bias in AI
14. AI Tools and Frameworks
15. AI Applications
16. Data Pre-processing
17. Model Evaluation and Metrics
18. Deployment and Ethical Considerations
19. Emerging Trends in AI
20. TensorFlow
21. Supervised and Unsupervised learning
22. Robotics with AI
23. Drones and Driving Cars with AI
24. Case Study: Information Technology

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