5 answers
7
Questions
Updated
211 views
how can I work in ai?
I'm asking this so i know what I can do to get there
Login to comment
5 answers
Updated
Liam’s Answer
I think Teklemuz's answer is a pretty complete guide on how to get deep into the industry. I feel that if you can use AI effectively and you can use it to solve a problem efficiently, you are working with AI. Other than the set of skills he has listed, think about having a prompt library, how to make and share skills, and how to fine tune existing models. I really feel like this is a field where you can start from nothing and get to something quickly. I don't think AI will be able to solve all of the problems in the world and itself is creating new problems we have not had before. If you set about figuring out what it is capable of and is able to assist you or clients with, you can really make something from starting with just a prompt.
Updated
Teklemuz Ayenew’s Answer
Starting a career in AI is an exciting journey that begins with learning Python and understanding basic math like statistics, probability, and linear algebra. Dive into key machine learning concepts to see how models learn, get tested, and improve. Hands-on practice with tools like NumPy and pandas is crucial. Focus on understanding data and the problems you want to solve, as this is the heart of AI work.
Joining hackathons, workshops, and internships gives you valuable real-world experience early on. As you advance, explore areas like deep learning, natural language processing, and computer vision. Learn to deploy models in real applications and build projects to enhance your portfolio.
Showcase your work using Git and GitHub and gain experience on platforms like Kaggle, Hugging Face, and DrivenData. Over time, develop software engineering skills, learn about system integration, and get familiar with cloud or MLOps concepts. Focus on a specific area like NLP or computer vision if it interests you.
In this rapidly evolving field, continuous learning, strong communication, and responsible AI awareness are key. Becoming skilled in AI takes steady effort and consistency. By learning constantly, building projects, gaining hands-on experience, and networking through LinkedIn, Stack Overflow, and GitHub, you can grow into a confident and job-ready AI professional.
Joining hackathons, workshops, and internships gives you valuable real-world experience early on. As you advance, explore areas like deep learning, natural language processing, and computer vision. Learn to deploy models in real applications and build projects to enhance your portfolio.
Showcase your work using Git and GitHub and gain experience on platforms like Kaggle, Hugging Face, and DrivenData. Over time, develop software engineering skills, learn about system integration, and get familiar with cloud or MLOps concepts. Focus on a specific area like NLP or computer vision if it interests you.
In this rapidly evolving field, continuous learning, strong communication, and responsible AI awareness are key. Becoming skilled in AI takes steady effort and consistency. By learning constantly, building projects, gaining hands-on experience, and networking through LinkedIn, Stack Overflow, and GitHub, you can grow into a confident and job-ready AI professional.
Updated
Sandeep’s Answer
Hello Jaden,
To work in AI, start by building a strong foundation in programming, mathematics, and data analysis. Many people begin by learning languages like Python and understanding basic concepts in statistics and machine learning.
From there, work on small projects such as analyzing datasets or building simple AI models. Sharing your projects on GitHub/Bitbucket can help you demonstrate your skills and start building experience in the field.
To work in AI, start by building a strong foundation in programming, mathematics, and data analysis. Many people begin by learning languages like Python and understanding basic concepts in statistics and machine learning.
From there, work on small projects such as analyzing datasets or building simple AI models. Sharing your projects on GitHub/Bitbucket can help you demonstrate your skills and start building experience in the field.
Updated
Maricela’s Answer
Hi Jaden,
Working in AI is a great goal, and there are many ways to get there. A good starting point is to build basic tech skills, especially:
-Programming (Python is the most common for AI)
-Math and logic (especially statistics)
-Problem-solving skills
You don’t have to learn everything at once. Start small: take online courses, try simple projects, and practice consistently.
Also, rememberthat AI isn’t just coding. There are roles in:
Data analysis
Business/strategy
Product management
So you can combine AI with your interests.
In simple terms: learn the basics, build small projects, and stay consistent. That’s how most people get started in AI.
Working in AI is a great goal, and there are many ways to get there. A good starting point is to build basic tech skills, especially:
-Programming (Python is the most common for AI)
-Math and logic (especially statistics)
-Problem-solving skills
You don’t have to learn everything at once. Start small: take online courses, try simple projects, and practice consistently.
Also, rememberthat AI isn’t just coding. There are roles in:
Data analysis
Business/strategy
Product management
So you can combine AI with your interests.
In simple terms: learn the basics, build small projects, and stay consistent. That’s how most people get started in AI.
Updated
Paranjyoti’s Answer
Working in AI is definitely within reach, and here's how you can get started:
1. Choose Your Path
- Technical AI: If you love math and coding, consider roles like Machine Learning Engineer or AI Researcher. You'll build models using tools like Python and TensorFlow.
- Data/Applied AI: For those who enjoy mixing coding with business thinking, roles like Data Scientist or AI/Analytics Consultant are great. You'll apply models to solve business problems.
- AI + Business: If you're more into decision-making and strategy without heavy coding, look into roles like Product Manager or Consultant.
2. Essential Skills
- Learn Python, statistics, and probability.
- Get comfortable with data handling using SQL and pandas.
- Understand basic machine learning concepts like regression and classification.
- Remember, you can learn these skills step by step.
3. Start with Simple Projects
- Begin with projects like predicting housing prices or building a spam email classifier.
- Move on to more complex tasks like recommendation systems or NLP projects.
- Projects show your ability to apply AI, which is crucial.
4. Learn by Doing
- While courses are helpful, building and showcasing projects will set you apart.
- Share your work on platforms like GitHub and be ready to explain your projects.
5. Gain Experience Early
- Look for internships, research opportunities, or freelance work related to data or AI.
- Even small experiences can make a big difference.
6. Set Realistic Timelines
- First 6 months: Focus on basics like Python and simple projects.
- 6-12 months: Work on solid projects and intermediate machine learning.
- 12+ months: Aim for internships or entry-level roles.
7. How to Stand Out
- Build multiple projects and understand why you chose certain methods.
- Combine AI with fields like finance or healthcare to broaden your expertise.
In summary, to thrive in AI, pick a path that suits you, learn the core skills, work on real projects, and gain practical experience. You don't need to have everything figured out right now—just start building, and your path will become clearer.
1. Choose Your Path
- Technical AI: If you love math and coding, consider roles like Machine Learning Engineer or AI Researcher. You'll build models using tools like Python and TensorFlow.
- Data/Applied AI: For those who enjoy mixing coding with business thinking, roles like Data Scientist or AI/Analytics Consultant are great. You'll apply models to solve business problems.
- AI + Business: If you're more into decision-making and strategy without heavy coding, look into roles like Product Manager or Consultant.
2. Essential Skills
- Learn Python, statistics, and probability.
- Get comfortable with data handling using SQL and pandas.
- Understand basic machine learning concepts like regression and classification.
- Remember, you can learn these skills step by step.
3. Start with Simple Projects
- Begin with projects like predicting housing prices or building a spam email classifier.
- Move on to more complex tasks like recommendation systems or NLP projects.
- Projects show your ability to apply AI, which is crucial.
4. Learn by Doing
- While courses are helpful, building and showcasing projects will set you apart.
- Share your work on platforms like GitHub and be ready to explain your projects.
5. Gain Experience Early
- Look for internships, research opportunities, or freelance work related to data or AI.
- Even small experiences can make a big difference.
6. Set Realistic Timelines
- First 6 months: Focus on basics like Python and simple projects.
- 6-12 months: Work on solid projects and intermediate machine learning.
- 12+ months: Aim for internships or entry-level roles.
7. How to Stand Out
- Build multiple projects and understand why you chose certain methods.
- Combine AI with fields like finance or healthcare to broaden your expertise.
In summary, to thrive in AI, pick a path that suits you, learn the core skills, work on real projects, and gain practical experience. You don't need to have everything figured out right now—just start building, and your path will become clearer.