By skyforbes 
This weekend I created a complete roadmap for anyone to thrive in the world of AI, Machine Learning, and Generative AI.
This guide outlines the key technologies, tools, and frameworks across 10 essential domains that every aspiring AI professional should learn:
1. NLP & Generative AI
Master tools like Hugging Face, OpenAI, Claude, and Gemini for LLMs. Build LLM-powered applications using LangChain or LlamaIndex.
2. IDEs & Tools
Use Jupyter, VS Code, and Kaggle for development and experimentation. Tools like Notion and Obsidian help manage prompt libraries and notes.
3. Core AI/ML Libraries
Learn foundational libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and tools like XGBoost and LightGBM.
4. Data Engineering & Storage
Work with databases (SQL, MongoDB), big data tools (Spark, Kafka), and warehousing platforms (BigQuery, Snowflake, Delta Lake).
5. Programming & Scripting
Master Python for ML, SQL for data querying, and scripting languages like R and Bash for automation.
6. Data Visualization
Use Matplotlib, Seaborn, Plotly, Power BI, and Dash to tell compelling data stories.
7. Data Analysis & Cleaning
Become proficient in tools like OpenRefine, Trifacta, Polars, and Dask to clean and prep data for analysis.
8. MLOps & Deployment
Learn deployment with tools like Docker, Kubernetes, MLflow, DVC, FastAPI, and cloud platforms like AWS, Azure, and Vertex AI.
9. Generative AI Tools & Ecosystem
Explore prompt platforms (PromptPerfect), AI agent builders (AutoGPT, LangGraph), and no-code AI platforms like Make.com and Zapier AI.
Whether you’re just starting out or scaling your AI expertise – this stack equips you for real-world AI problem-solving.
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