How to find a complete roadmap to learn Open AI?

Question

Hi, I am Muhammad Adnan, having 10+ years experience as .NET software engineer. Now I want to learn Open AI or AI to upgrade my skills.
So I want a complete roadmap of all Topics step by step.

Answer 1

Use https://learn.microsoft.com/en-us/training/browse/?terms=openai

---

If the above response helps answer your question, remember to "Accept Answer" so that others in the community facing similar issues can easily find the solution. Your contribution is highly appreciated.

hth

Marcin

Answer 2

Hey @rao adnan ,
It’s great to hear that you want to dive into the world of Open AI and upgrade your skills. Here’s a roadmap that can help you get started with learning AI, especially focusing on Azure OpenAI capabilities:
Step 1: Foundation – Programming and Math

Python programming: Learn Python, the most widely used AI/ML language.

Mathematics for AI: Study linear algebra, statistics, probability, and basic calculus, these are critical for understanding algorithms and models.​

Data structures & algorithms: Get comfortable with common data structures and how to work with data programmatically.​

Step 2: Data Science & Machine Learning Core

Data Manipulation: Practice with NumPy, pandas, and matplotlib for handling and visualizing data.

Machine Learning (ML): Learn supervised and unsupervised learning, regression, classification, clustering, decision trees, and ensemble methods.

Model Evaluation: Understand metrics like accuracy, precision, recall, F1-score, ROC/AUC, and cross-validation.​

Step 3: Deep Learning & Specialized Areas

Deep Learning: Master neural networks, especially feed-forward, convolutional (CNNs), recurrent (RNNs), and transformers.

Natural Language Processing (NLP): Study text preprocessing, sentiment analysis, and language models.

Computer Vision: Learn how to process images, object detection, segmentation, and apply open-source libraries like OpenCV.​

Step 4: Generative AI and OpenAI Technologies

Large Language Models (LLMs): Explore how models like GPT and other foundational OpenAI architectures work, their strengths, and prompt engineering for LLMs.

Fine-tuning Models: Learn transfer learning, model fine-tuning, and customizing AI for specific tasks or datasets.

Generative AI Projects: Hands-on with image generation (diffusion models), AI chatbots, and multi-modal AI (image + text).​

Step 5: MLOps & Production

Model Deployment: Learn to deploy models with Flask/FastAPI, Docker, and cloud (Azure, AWS, or GCP).

Monitoring & Scaling: Understand model monitoring, retraining, and scalable AI infrastructure.

Experiment Tracking & Versioning: Explore tools like MLflow for managing machine learning lifecycles.​

Step 6: Trends, Agentic AI & Advanced Skills

Agentic AI: Study autonomous agents, reinforcement learning, and the orchestration of multi-agent systems.

Ethics & Responsible AI: Learn about bias, fairness, and ethical issues specific to AI.

Stay Updated: Follow OpenAI’s updates (e.g., upcoming GPT-5, multi-modal models, and new platform tools) for the latest technology shifts.

Helpful Resources:

• Introduction to AI Concepts
• Introduction to Machine Learning Concepts
• Get Started with Azure AI Services
• Develop Generative AI Solutions with Azure OpenAI
• Introduction to Natural Language Processing Concepts
• Develop Applications with Azure OpenAI
• Use AI Responsibly with Azure Content Safety

Hope this roadmap helps you get started! If you have any more specific questions or need guidance on certain topics, feel free to ask!