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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

programming computerscience ai datascience python artificialintelligence machinelearning machine-learning softwaredevelopment webdevelopment dataanalytics careerbuilding learnbydoing
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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!
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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.
Thank you comment icon Thank you so much for the advice. Abab
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Nalini’s Answer

Hey Abab, Since you are already studying Computer Science and building websites, you already have a strong start. If you want to go into AI or machine learning, I would suggest do not wait until you feel like you know everything. Start now by using AI tools, coding assistants, notebooks, which are currently available at ease. Start building simple things like chatbots, AI websites, or dashboards that brings impact and always bring your enthusiasm and curiosity to the place. Also, since you are still pursuing your studies, now is the time to create real projects as your thesis or the internship program and show them to others. Publish your work on GitHub, deploy your projects online, and write a short explanation of what each one does. My Advice: Always go to a place as if you are learning... donot go as if you know everything
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PARTH’s Answer

My recommendation: don’t aim first for “pure AI researcher.” Aim for applied AI engineer / software engineer with AI skills. That path is more realistic for a CS student, fits your website-building experience, and matches where hiring is going: AI job demand is growing fast, but true entry-level roles are more competitive and employers increasingly want proof that you can build real products, not just know theory

Here’s the practical path:

Keep your web development skills. They are not separate from AI; they make you more employable because companies want people who can turn AI models into usable apps. A student who can build a website, API, database flow, and AI feature is stronger than someone who only knows notebooks.
Master the core stack first: Python, SQL, Git, APIs, and one cloud platform. These are the non-negotiables across AI, data, and software roles.
Then add AI/ML depth: statistics, machine learning basics, Pandas, scikit-learn, TensorFlow or PyTorch, and basic LLM/RAG concepts. Employers increasingly value practical ML and generative AI skills, not just coursework.
Learn deployment, not just modeling. MLOps, Docker, CI/CD, and cloud deployment matter because companies hire for production value, not only model accuracy.
Build 3 strong portfolio projects. Best mix for you:

a web app with an AI feature,
a data science project with real data and business insights,
an end-to-end ML app deployed online.
Portfolios and documented GitHub work are major differentiators.


Get internships early. In this market, internships are close to essential, and they materially improve odds of full-time conversion.
Practice communication. Strong candidates explain what they built, why it matters, what trade-offs they made, and how the result helped users or business outcomes. Soft skills are consistently valued.

A smart career strategy for you would be:
Phase 1: Next 3 months

strengthen Python and SQL
keep building websites
learn ML basics
publish everything on GitHub

Phase 2: Next 3–6 months

build AI web apps like chatbot + document search, recommendation system, or resume analyzer
learn one cloud platform
learn Docker and API deployment

Phase 3: Next 6–12 months

apply for internships, entry-level SWE, data, and AI-engineer roles
target roles like Software Engineer, Data Analyst, Junior Data Scientist, ML Intern, Applied AI Intern
do not restrict yourself only to jobs titled “AI Engineer,” because those are fewer at entry level.

The key trade-off: if you go too narrow too early, you may miss jobs; if you build a software + AI profile, you keep more doors open while still moving toward AI. That is the stronger early-career bet in today’s market.

Pick one target role: applied AI engineer, data scientist, or software engineer with AI focus.
Start one portfolio project that combines your web skills and AI.
Create or clean up your GitHub and LinkedIn.
Apply for internships and junior roles consistently, even if you don’t meet 100% of requirements.
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Ryan’s Answer

Hello,

If you are really interested in AI/ML, then I would suggest a few things.

First thing is to learn and read different material relating to AI/ML whether it be the latest news or published research paper. It's important to stay up to date on this topic as it is always progressing day by day. There are many different efforts using AI/ML so it would help to try and narrow down something you are more interested in such as Computer Vision, LLMs, etc.. Try to learn the basics of underlying AI models and architecture such as what a neural network is or what a transformer is. At the end of the day, AI/ML is more math than programming, so it's important to understand what you are getting into if you want to become an AI engineer rather than a developer that uses AI to code. There are tons of free resources to learn about these things and how they work online and on youtube. Just pick a different topic every week and start learning a little bit more about the topic every day. Lastly, make sure to be using and experimenting with these technologies. This will help you get a better understanding of how they work and what they do.
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Sharadha’s Answer

To build a successful career in AI/ML, first get strong in Python, SQL, math, and coding, then make real AI projects and websites, gain internship or freelance experience, and apply for beginner roles where you can keep learning and growing.
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Eashan’s Answer

Here's a simple plan to kickstart your AI-ML career using your computer science and web skills:

1. Master AI/ML Basics
Get good at Python, basic statistics, linear algebra, and key machine learning tools like NumPy, Pandas, and Scikit-learn. These will help you understand how models work with data.

2. Create AI-Driven Web Apps
Leverage your web development skills to build sites that use AI APIs like Gemini or Hugging Face. Designing easy-to-use interfaces for AI models is a valuable skill.

3. Learn Model Deployment
Understand how to package, host, and deploy models to the cloud with tools like Docker. Companies need engineers who can make models work in real-world settings.

4. Build a GitHub Portfolio
Develop and share 2 or 3 projects, like a smart chatbot or a prediction tool. Showing your work on GitHub is the best way to demonstrate your skills to potential employers.
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Jacob’s Answer

Hey!
I've actually been in your exact position, so I want to give you an honest answer rather than the generic advice you'll find everywhere else.
First, a word of transparency about the current AI landscape:

With the explosion of LLMs as a service, a significant portion of what gets labeled "AI engineering" in the job market right now is, frankly, writing a wrapper around an API call to OpenAI. That's a real thing to be aware of as you're setting your career expectations. If your goal is to actually build AI software — the models, the architectures, the pipelines — you'll need to be more deliberate about how you get there.

My background, and why it shapes this advice:
I worked at a large defense contractor where I had the opportunity to work with AI and computer vision systems in the more traditional, non-LLM sense — custom-built models, real engineering challenges. That experience genuinely sparked my passion for the field, and it eventually led me to pursue and complete a Master's in AI at SJSU. I say this not to suggest everyone needs a Master's, but to give you context for where this advice is coming from.

So, how do you actually build a career in real AI/ML?
1. Nail Your Mathematical Foundation
This is non-negotiable. Linear algebra, calculus, probability, and statistics are the engine underneath every ML model. Your Data Analytics degree will give you a head start here — lean into it hard.

2. Learn the Core ML Stack
Get hands-on with Python, NumPy, pandas, and then move into frameworks like PyTorch or TensorFlow. Don't just follow tutorials — implement things from scratch when you can. Building a neural network by hand before using a framework is how you actually understand what's happening.

3. Go Beyond LLMs — Study Traditional AI/ML
Computer vision, reinforcement learning, time series forecasting, recommendation systems — these areas are where a lot of genuinely deep engineering work still happens. Read papers on arXiv. The research is freely available, and this is how you start thinking like someone who builds AI rather than someone who uses it.

4. Build a Portfolio of Real Projects
Your web development background is actually an asset here — you can build end-to-end projects that combine a trained model with a real interface. A deployed, working project that solves a real problem is worth more than a dozen tutorial completions on your resume.

5. Choose Your Path Intentionally
There are two realistic routes to doing genuine AI work:

Find a role that's actually building it — defense, robotics, healthcare, autonomous systems, and research-oriented companies tend to do real ML engineering. Be skeptical of job postings that are vague about what the "AI work" actually involves.

Pursue it independently alongside your career — you don't need a Master's degree, but you do need the equivalent rigor. Online resources, research papers, Kaggle competitions, and open-source contributions can get you there if you're disciplined and consistent.

Both paths are legitimate. The Master's route gives you structure, credibility, and access to research networks. The self-directed route gives you flexibility and speed. What matters most is that you're genuinely building things, not just consuming content about AI.
Your Data Analytics background is a stronger foundation than most people starting out realize. Use it, and keep building.

Good luck — the field rewards people who go deeper than the surface level.
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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!
Thank you comment icon Thanku for you valuable reply... Abab
Thank you comment icon To land a successful job in AI-ML as a computer science student with web development skills, focus on becoming an AI Engineer who bridges the gap between machine learning models and user interfaces. Start by integrating AI APIs into your web projects to build functional, full-stack applications. Simultaneously, master Python, data cleaning, and core ML libraries like scikit-learn. Host these projects live on platforms like GitHub and Hugging Face so recruiters can test them. Vikas Nadvi
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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.
Thank you comment icon Thank you for your valuable information Abab
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Rohan’s Answer

Hey Abab, Glad to see you are already thinking about next steps, Below is the plan for you, do not wait for networking you can start that now..

Month 1–2: Strengthen Python, SQL, data structures, and ML basics.
Month 3: Choose your industry, Build one ML project which are industry specific end-to-end and publish it on different platforms.
Month 4: Learn LLMs, prompting, embeddings, and RAG; build one AI web app.
Month 5: Improve deployment skills with Git, Docker, and cloud basics.
Month 6: Polish 3 portfolio projects, update LinkedIn/GitHub, and apply for internships and junior AI/software roles.
Use your website-building skills to make AI products that are visible and usable. Consistent projects, not certificates alone, will get you hired.

Good Luck!
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Q’s Answer

Stay curious and keep learning because the field changes quickly. Strengthen your basics in programming, math, statistics, and data structures, then start applying them through small AI/ML projects.

A few practical steps: build a portfolio on GitHub, try projects like recommendation systems, chatbots, image classification, or data analysis dashboards, and learn tools like Python, SQL, TensorFlow/PyTorch, and cloud platforms. Also, practice explaining your work clearly because communication matters in every job.

Most importantly, don’t wait until you feel “ready.” Keep building, keep experimenting, ask questions, and learn from mistakes. Over time, your projects and consistency will help you stand out for internships and jobs.
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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.
Thank you comment icon Thanku for your valuable words and time... Abab
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Tariq’s Answer

Begin by exploring different types of models and their uses. With the help of the new AI coding assistant, it's now easier than ever. Choose a real-world area like data, voice, or video that interests you. Start with a small project and dive into your first AI or ML project. You've got this!
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Christopher’s Answer

Great question! The degree you are working on is broadly applicable meaning that you can use it anywhere and in any industry. My adivce would be to combine something you are passionate about in your life and combine it with you work interest - for example, if you are a big soccer fan - you can use AI/ML skills to analyze matches, determine efficiency and help apply these amazing technologies to the sports industry. A number of companies work in that space and thats just one example - other big ones are Music, Construction, Technology, Telecommunications, and financial services.
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Chris’s Answer

What a great question! It's not a secret that AI is making knowledge and software (development) cheap, so studying this field in college will help you develop the tooling muscles so that knowledge and software is cheap for you, when you start your career journey. Applying this in the real world requires other dimensions that AI won't replace so easily:

1. seeking out friction and using your developed AI muscles to solve for that friction
2. developing a teaming framework to amplify your impact with like-minded talent
3. innate work ethic to solve problem at order of magnitude (10X, not %'s) improvement

These are behaviors that you can start practicing in school while you are sharpening your tech skills. Apply it personally, your studies, your part time job - anything that you're spending time on. We all share the same amount of minutes per day, and how you choose to spend those minutes is a strong signal of future success in chasing your ambitions.

All the best!!
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Julian’s Answer

With your degree, you will have a strong grasp of software. A smart next step is to learn the key parts needed for a website, such as HTML, Angular, JavaScript, and CSS. Also, look for website-building software that uses AI tools. This will help you create a solid base, and you can make updates as you get more comfortable with the basics.
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Raghavendra’s Answer

Hi Abab - You're doing great with your Computer Science degree and website building! If you're excited about AI/ML, start by strengthening your basics like Python, SQL, data structures, and some math/statistics. Then, dive into small AI/ML projects on the side. Create projects you can showcase on GitHub, such as a simple chatbot, prediction model, or data analysis app. Participating in internships, hackathons, and staying updated with new tools will be very beneficial. The key is to keep learning, keep creating, and gain real experience whenever possible. Keep going, you're on the right track!
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Sumith’s Answer

We have wonderful responses here. To add another point, think why we are building this, for whom are we building this, how can we build (what other approaches are possible. These ways of thinking will help you grow faster in your career.

All the best :)
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Rachana’s Answer

Hello,

Build a strong foundation first: focus on math (linear algebra, calculus, probability), core CS (data structures/algorithms), and take solid AI/ML courses while doing small ML projects (Kaggle, personal datasets) and putting them on GitHub.
Then aim for internships and entry‑level roles by tailoring your resume/portfolio to ML (highlight models you built, tools like Python, NumPy, pandas, scikit‑learn, PyTorch/TensorFlow) and networking with people in AI/ML via LinkedIn, meetups, and university events.
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Olufunbi’s Answer

Focus on learning Python, SQL, and one machine learning stack like scikit-learn, while also building a few real projects that show you can solve problems, not just follow tutorials. Since you already build websites, use that to your advantage by turning your AI projects into simple apps, because being able to both build and explain your work will make you stand out to employers.
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