5 answers
Abab
Student
Kochi, Kerala, India
1
Question
Updated
1102 views
hey!I am doing computer science degree and doing some website building .I am more intersted in AI-ML,so how to achieve a succcesful job in the carrer life?
Now iam doing Bsc Cs Data Analytics
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5 answers
Dwayne Koh
Creative Direction
1
Answer
Los Angeles, California
Updated
Dwayne’s Answer
Hey! Since you’re already doing a Computer Science degree and building websites, you’re in a really good position. If you’re interested in AI/ML, don’t wait until you “know everything” before building. Start using the tools now: AI coding assistants, ML notebooks, APIs, model playgrounds, and try vibe coding small projects. You’ll be amazed how far you can go when you combine your ideas with the right tools.
For a successful AI/ML career, focus on three things:
My take is , create real projects: recommendation systems, chatbots, image classifiers, AI-powered websites, data dashboards, etc.
Show your work: put projects on GitHub, deploy them online, write short explanations, and keep improving.
Also, don’t ignore your web development skills. AI + web development is powerful because many companies need people who can turn AI ideas into real usable products.
My advice: keep learning, keep building, use AI tools actively, and make a portfolio that proves what you can do. That will help you more than just certificates alone!
For a successful AI/ML career, focus on three things:
My take is , create real projects: recommendation systems, chatbots, image classifiers, AI-powered websites, data dashboards, etc.
Show your work: put projects on GitHub, deploy them online, write short explanations, and keep improving.
Also, don’t ignore your web development skills. AI + web development is powerful because many companies need people who can turn AI ideas into real usable products.
My advice: keep learning, keep building, use AI tools actively, and make a portfolio that proves what you can do. That will help you more than just certificates alone!
Liam Rielly
Data Center Technician
180
Answers
Manassas, Virginia
Updated
Liam’s Answer
Keep on your path. Start to find practical solutions in business you can use AI to automate tasks. From a learner's standpoint, stick with what you are doing and just start to add AI projects into that. Make a locally hosted AI server and start to ask it to complete tasks. I think right now the two popular AI harnesses are Claude Code and Hermes. I think either will be a good starting point on top of all of the other learning you are doing.
Start to figure out how AI can assist someone with a job. Don't focus on a project that would replace an entire department, or a project that will make money itself for itself. Figure out what someone on a job does, a simple thing AI can do to help them, and how you can use code to assist with that job. Get a job where you can see what different people in an org do. Something like a warehouse job will show you different workers, management, and skilled laborers. Figure out that one thing they need help with and try to code something yourself to help that need. This will advance you further than any sort of "one stop software solution" project you make. Having a prompt library for a shift lead that automates most of their administrative tasks would be more functionally useful in my opinion.
This is just an idea, but the skies the limit. Focus on practical for everyone. Think about what a business needs. Think about labor level work and how you can help someone moving boxes and not trying to replace that worker with AI. Keep up with your studies because even if it seems like you are not doing anything AI related, you are doing everything to prepare you to work in that field!
Start to figure out how AI can assist someone with a job. Don't focus on a project that would replace an entire department, or a project that will make money itself for itself. Figure out what someone on a job does, a simple thing AI can do to help them, and how you can use code to assist with that job. Get a job where you can see what different people in an org do. Something like a warehouse job will show you different workers, management, and skilled laborers. Figure out that one thing they need help with and try to code something yourself to help that need. This will advance you further than any sort of "one stop software solution" project you make. Having a prompt library for a shift lead that automates most of their administrative tasks would be more functionally useful in my opinion.
This is just an idea, but the skies the limit. Focus on practical for everyone. Think about what a business needs. Think about labor level work and how you can help someone moving boxes and not trying to replace that worker with AI. Keep up with your studies because even if it seems like you are not doing anything AI related, you are doing everything to prepare you to work in that field!
Thanku for you valuable reply...
Abab
Terry Helm, ING. MIM, MTM
Solution Manager - Technical Consulting
13
Answers
Ballwin, Missouri
Updated
Terry’s Answer
Mastering AI and ML is crucial today, as businesses need employees skilled in these areas. Consider taking classes at your university or explore online options. Here are some of the best platforms for AI courses, depending on your goals:
1. For most learners, Coursera is a great choice, especially the Andrew Ng/DeepLearning.AI track. Non-technical learners can start with AI For Everyone, while those with some coding skills can try the Machine Learning Specialization. Aspiring AI engineers should look at the Deep Learning Specialization.
2. For practical skills in Generative AI, DeepLearning.AI offers short courses in prompt engineering and AI agents, with partners like OpenAI and Google.
3. For a free, hands-on experience, fast.ai's Practical Deep Learning for Coders is excellent. It focuses on building models first before diving into theory.
4. If you're looking for academic depth, edX offers rigorous courses like Harvard's Introduction to AI with Python and MIT's Introduction to Deep Learning.
5. For a high-level university credential, Stanford Online provides graduate-level AI courses, though they are more challenging and costly.
6. For building a portfolio and transitioning careers, Udacity's Nanodegrees are project-based and job-focused, best for those who already know the basics.
I recommend starting with Coursera/DeepLearning.AI unless you have specific needs. Non-technical learners should try AI For Everyone. If you can code, go for the Machine Learning Specialization. For quick, budget-friendly learning, choose fast.ai. For prestige and rigor, consider Stanford Online or edX. Choose based on your current skill level to avoid wasting time.
1. For most learners, Coursera is a great choice, especially the Andrew Ng/DeepLearning.AI track. Non-technical learners can start with AI For Everyone, while those with some coding skills can try the Machine Learning Specialization. Aspiring AI engineers should look at the Deep Learning Specialization.
2. For practical skills in Generative AI, DeepLearning.AI offers short courses in prompt engineering and AI agents, with partners like OpenAI and Google.
3. For a free, hands-on experience, fast.ai's Practical Deep Learning for Coders is excellent. It focuses on building models first before diving into theory.
4. If you're looking for academic depth, edX offers rigorous courses like Harvard's Introduction to AI with Python and MIT's Introduction to Deep Learning.
5. For a high-level university credential, Stanford Online provides graduate-level AI courses, though they are more challenging and costly.
6. For building a portfolio and transitioning careers, Udacity's Nanodegrees are project-based and job-focused, best for those who already know the basics.
I recommend starting with Coursera/DeepLearning.AI unless you have specific needs. Non-technical learners should try AI For Everyone. If you can code, go for the Machine Learning Specialization. For quick, budget-friendly learning, choose fast.ai. For prestige and rigor, consider Stanford Online or edX. Choose based on your current skill level to avoid wasting time.
Thank you for your valuable information
Abab
Sandeep Khandelwal
Software Engineer
131
Answers
San Ramon, California
Updated
Sandeep’s Answer
Hello Abab,
You are already on a good path since you’re learning computer science, data analytics, and web development together. To build a successful career in AI/ML, focus on strengthening your skills in Python, machine learning, statistics, and real-world projects.
Try building small AI projects, participate in internships, and share your work on GitHub/Bitbucket. Companies usually value practical experience and problem solving skills more than just degrees or certificates.
You are already on a good path since you’re learning computer science, data analytics, and web development together. To build a successful career in AI/ML, focus on strengthening your skills in Python, machine learning, statistics, and real-world projects.
Try building small AI projects, participate in internships, and share your work on GitHub/Bitbucket. Companies usually value practical experience and problem solving skills more than just degrees or certificates.
Thanku for your valuable words and time...
Abab
Teklemuz Ayenew Tesfay
Software Engineer and Career Mentor
654
Answers
Ethiopia
Updated
Teklemuz Ayenew’s Answer
You're already on a great path with your background. To move into AI and machine learning, start by building a strong base in data structures and algorithms, applied math, Python programming, and data handling. Then, learn the basics of machine learning and gradually move to advanced deep learning.
Keep practicing by working on real-world projects like spam filters, recommendation systems, and chatbots. Build a strong portfolio and use platforms like Kaggle, Google Colab, GitHub, and Hugging Face to practice and show your work. Join workshops and programs from Google Developer Groups, Microsoft Learn Student Ambassadors, AWS training, and AI bootcamps for hands-on experience.
Get practical experience through hackathons, virtual internships, fellowships, collaborations, and open-source projects. Share your progress on LinkedIn, and follow a clear path from data roles to machine learning roles, eventually specializing in areas like Natural Language Processing or Computer Vision. Consistent learning and problem-solving, along with real-world application, will set you up for a successful career in machine learning. Stay confident, curious, and eager to learn.
Keep practicing by working on real-world projects like spam filters, recommendation systems, and chatbots. Build a strong portfolio and use platforms like Kaggle, Google Colab, GitHub, and Hugging Face to practice and show your work. Join workshops and programs from Google Developer Groups, Microsoft Learn Student Ambassadors, AWS training, and AI bootcamps for hands-on experience.
Get practical experience through hackathons, virtual internships, fellowships, collaborations, and open-source projects. Share your progress on LinkedIn, and follow a clear path from data roles to machine learning roles, eventually specializing in areas like Natural Language Processing or Computer Vision. Consistent learning and problem-solving, along with real-world application, will set you up for a successful career in machine learning. Stay confident, curious, and eager to learn.
Thank you so much for the advice.
Abab