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AI/ML Learning Paths
Computer Vision Learning Path
Sub Domains
Image Processing, Object Detection, Face Recognition
Industry Job Roles
Computer Vision Engineer, AI Researcher
Micro-Credential A: Tool Level
Getting Started with Computer Vision using TensorFlow
- Introduction to Computer Vision and image data basics
- Working with image datasets using deep learning frameworks
- Understanding convolutional neural networks (CNNs)
- Building basic image classification models
- Introduction to Computer Vision and image data basics
- Working with image datasets using deep learning frameworks
- Understanding convolutional neural networks (CNNs)
- Building basic image classification models
Micro-Credential B: Domain Level
Applied Computer Vision for Real-World Problems
- Image Classification and Multi-label Classification
- Object Detection and Localization Techniques
- Semantic Segmentation and Instance Segmentation
- Image Classification and Multi-label Classification
- Object Detection and Localization Techniques
- Semantic Segmentation and Instance Segmentation
Micro-Credential C: Sub Domain Level
Advanced Computer Vision Applications
- Image Processing
- Object Detection
- Face Recognition
- Image Processing
- Object Detection
- Face Recognition
Capstone Projects
- Real-Time Object Detection for Retail Inventory Monitoring
- Medical X-ray Image Classification for Disease Detection”
- Autonomous Vehicle Lane & Traffic Sign Detection
Entry Qualifications
B.Tech/M.Tech in AI, CS, or related fields
Exit Qualifications
Certified Computer Vision Engineer, NVIDIA AI Specialist
Potential Employers
Tesla, OpenAI, Google Research
Natural Language Processing (NLP) Learning Path
Sub Domains
Conversational AI, Large Language Models, Sentiment Analysis
Industry Job Roles
NLP Engineer, AI Chatbot Developer
Micro-Credential A: Tool Level
Foundations of NLP with Pre-trained Language Models:
- Understanding Transformer Architecture and Tokenization
- Applying Pre-trained Models for NLP Tasks (e.g., Text Generation, Classification, QA)
- Deploying NLP Solutions via APIs and Ensuring Responsible AI Use
- Understanding Transformer Architecture and Tokenization
- Applying Pre-trained Models for NLP Tasks (e.g., Text Generation, Classification, QA)
- Deploying NLP Solutions via APIs and Ensuring Responsible AI Use
Micro-Credential B: Domain Level
Applied NLP for Real-World Use Cases:
- Information Extraction and Named Entity Recognition (NER)
- Text Summarization and Machine Translation Techniques
- NLP in Domain-Specific Applications (Healthcare, Legal, Education, etc.)
- Information Extraction and Named Entity Recognition (NER)
- Text Summarization and Machine Translation Techniques
- NLP in Domain-Specific Applications (Healthcare, Legal, Education, etc.)
Micro-Credential C: Sub Domain Level
Advanced NLP Specializations:
- Conversational AI
- LLMs
-Sentiment Analysis
- Conversational AI
- LLMs
-Sentiment Analysis
Capstone Projects
- Smart Customer Support Chatbot for E-Commerce
- Legal Document Summarizer & Entity Extractor
- Sentiment Dashboard for Social Media Monitoring
Entry Qualifications
B.Tech/M.Tech in AI, Computational Linguistics, or CS
Exit Qualifications
Certified NLP Engineer, OpenAI GPT Specialist
Potential Employers
Microsoft, OpenAI, Facebook AI