Certifications Guide
| Certification | Provider | Description | Time to complete |
|---|---|---|---|
| A Mind Map of Core Machine Learning Concepts | Ruslan Diachenko | This blog post provides a visual mind map outlining core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It serves as a beginner-friendly roadmap that connects key topics, methods, and tools like scikit-learn, TensorFlow, and PyTorch. |
blog |
| Academic Listening and Note Taking | Coursera | This course is specifically geared to help non-native English speakers improve their listening and note-taking skills when attending academic lectures. Learn how to take better lecture notes, techniques for improving your understanding, and how to give an effective academic presentation. |
32 hours |
| AI Applications and Prompt Engineering | edX | Teaches how to build AI tools and write effective prompts for AI systems. |
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| AI Applications and Prompt Engineering | edX | Teaches how to build AI tools and write effective prompts for AI systems. |
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| AI for Beginners | Microsoft Learn | Modules on cloud computing, AI, and data science for university students to gain hands-on experience. |
30 hours |
| AI Infrastructure and Operations Fundamentals | Coursera | Introduces AI concepts, explores data center and cloud infrastructures, and prepares learners for the NVIDIA-Certified Associate certification. |
10 hours |
| AI, ML, DL and Data Science | A video by Simplilearn breaking down and distinguishing artificial intelligence, machine learning, deep learning, and data science. |
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| Augment your LLM with Retrieval Augmented Generation | A workshop covering foundational tools, libraries, and techniques for AI/graphics development, delivered over multiple sessions. |
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| Augment your LLM with Retrieval Augmented Generation | A workshop covering foundational tools, libraries, and techniques for AI/graphics development, delivered over multiple sessions. |
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| Big Data, Artificial Intelligence, and Ethics | Coursera | Learn about the ethical challenges of AI and big data through case studies, NLP applications, and examining their impact on society. |
9 hours |