5 answers
7
Questions
Updated
281 views
skills needed to work in ai?
after looking you need a mix of technical expertise
Login to comment
5 answers
Updated
Manu Nicholas’s Answer
Assuming you mean working on the backend of AI and building tools with it I would say the main langauage a lot of tool work with is Python. Get comfortable with it and learn NumPy, Pandas, PyTorch, and TensorFlow.
Understand machine learning fundamentals before jumping into deep learning. Regression, classification, clustering, how models are trained and evaluated. A lot of people skip this and wonder why nothing makes sense later.
Then go deeper into neural networks, transformers, and whatever area of AI excites you whether that's NLP, computer vision, robotics, or something else. Pick a lane and build projects in it.
Also learn how to read research papers. AI moves fast and the latest stuff hits ArXiv long before it becomes a tutorial.
Also build projects and contribute to Open source repositories on github. these are the best ways to stay ahead of the pack
Understand machine learning fundamentals before jumping into deep learning. Regression, classification, clustering, how models are trained and evaluated. A lot of people skip this and wonder why nothing makes sense later.
Then go deeper into neural networks, transformers, and whatever area of AI excites you whether that's NLP, computer vision, robotics, or something else. Pick a lane and build projects in it.
Also learn how to read research papers. AI moves fast and the latest stuff hits ArXiv long before it becomes a tutorial.
Also build projects and contribute to Open source repositories on github. these are the best ways to stay ahead of the pack
Updated
Mari’s Answer
To succeed in AI, you need both technical and soft skills. Focus on learning Python, SQL, machine learning, prompt engineering, data handling, and basic cloud and deployment knowledge. Also, work on your communication, critical thinking, adaptability, and ability to explain AI results clearly.
Updated
PARTH’s Answer
To work in AI, you need a mix of technical, data, and practical delivery skills:
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.
Updated
Neli’s Answer
Prompt engineering and machine learning are a must in my opinion.
Updated
Manasa’s Answer
1. Understanding the basics of AI and programming is essential. You should be able to read code, grasp its purpose, and assess its efficiency.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.