u/TusharKharade_

Hey everyone!

I’m planning to start learning Machine Learning by actually building projects instead of spending too much time going through courses and theory before building anything.

I already know Python, NumPy, Pandas, Matplotlib, and Seaborn, and I also have some experience with data collection and data cleaning. Right now, I’m working on my probability and math fundamentals as well.

My long-term goal is to become an AI/ML Engineer, so I want to learn ML in a practical way and gradually work my way toward more advanced projects.

I’d really appreciate some suggestions from people who have already gone through this:

• What ML projects would you recommend starting with?
• How would you recommend progressing from basic → intermediate → advanced projects?
• Are there any projects that helped you understand ML concepts deeply?
• I’d especially love GitHub repositories where I can look at good ML projects, learn from the code, and try implementing them myself.
• Any good real-world datasets or practical projects would also be helpful.

I’m not looking for projects where I just load a dataset and call model.fit() 😅. I want projects that actually make me understand why the model works, how to improve it, and how ML is used to solve a real problem.

If you learned ML through a build-first approach, I’d love to hear what worked for you and what you would recommend to someone starting out.

Thanks! 🙌

Hey everyone!

I’m planning to start learning Machine Learning by actually building projects instead of spending too much time going through courses and theory before building anything.

I already know Python, NumPy, Pandas, Matplotlib, and Seaborn, and I also have some experience with data collection and data cleaning. Right now, I’m working on my probability and math fundamentals as well.

My long-term goal is to become an AI/ML Engineer, so I want to learn ML in a practical way and gradually work my way from beginner projects to more advanced ones.

I’d really appreciate some suggestions from people who have already gone through this:

• What ML projects would you recommend starting with?
• How should I progress from beginner → intermediate → advanced?
• Are there any projects that actually helped you understand ML concepts deeply?
• I’d especially love GitHub repositories where I can look at good ML projects, learn from the code, and maybe try implementing them myself.
• Any good real-world datasets or project ideas would also be helpful.

I’m not looking for projects where I just load a dataset and call model.fit() 😅. I want projects that actually make me understand why the model works, how to improve it, and how ML is used in a real problem.

If you learned ML through a build-first approach, I’d love to hear what worked for you and what you would recommend to someone starting out.

Thanks! 🙌

Hi everyone,

I’m currently pursuing my MCA and I’ll be graduating around mid-2028. I haven’t specialized in any particular stream yet, so I still have enough time to choose a direction and build my skills properly.

Right now, I’m confused between Machine Learning/AI and Backend Development.

The problem is that I would have to learn both almost from the beginning. I’m considering building projects in both areas initially to understand which one suits me better, but I don’t want to spend a year jumping between technologies without becoming job-ready in either.

My current goal is to get a good software/tech job after my MCA, preferably with strong salary growth and good long-term career prospects.

For people already working in the industry or who recently graduated:

• How is the job market for ML/AI vs Backend Development for freshers?
• Which one has better opportunities for someone graduating in 2028?
• Is ML/AI realistically worth pursuing as a fresher, considering the entry-level competition and requirements?
• Would focusing on Java + Spring Boot + Backend + DSA + Cloud be a safer route?
• Should I build projects in both first and then decide, or commit to one path early?
• What skills/projects would actually make an MCA fresher stand out in either field?

I’m not looking for a “follow your passion” answer 😅. I’d really appreciate practical advice based on the current job market and what you think the market might look like around 2028.

Thanks!

I’m currently in a bit of a career confusion phase and would really appreciate some honest advice from people already working in the industry.

I’ve completed my BCA and I’m currently pursuing my MCA from DAVV IIPS Indore. My MCA will be completed around mid-2028, so I still have around 2 years to properly prepare.

The problem is that I’ve jumped between different paths quite a lot.

At different points, I’ve studied Android development, Java, C++, and DSA. I’ve done basic DSA in both C++ and Java, but I never really stuck with one path long enough to become good at it.

Over the last couple of months, I started exploring Data Analytics/Data Science. So far, I’ve learned things like:

• MS Excel
• Data visualization
• Basic data collection
• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Basic SQL
• Basic DSA

Now I'm at the point where I want to stop jumping between technologies and seriously commit to one career path.

My main goal is pretty straightforward: I want a good-paying and relatively stable IT job after my MCA.

I’ll obviously participate in my on-campus placements, but I don't want my career to depend completely on campus placements. I also want to prepare properly for off-campus opportunities, including product companies and good startups.

I’m willing to put in around 8 hours a day consistently for the next 2 years if that's what it takes.

But I'm confused about what I should actually focus on.

Should I go towards:

• Software Engineering / Backend Development
• Data Analyst → Data Scientist
• AI/ML Engineering
• Cloud/DevOps
• AI Infrastructure / AI Software Engineering
• Or something else?

I'm not necessarily looking for the "easiest" path. I'm willing to work hard. What I want is a path that gives me a good chance of getting a job as a fresher, decent salary growth, and reasonable long-term stability.

If you were in my position — BCA completed, MCA finishing in 2028, basic programming/DSA + some Data Science knowledge, and 8 hours/day available to study — what would you choose?

Also, if possible, I'd really appreciate advice on:

1. Which role would you target?
2. What skills would you learn from now until 2028?
3. How important is DSA for that role?
4. What kind of projects would actually help with placements/off-campus applications?
5. Should I focus more on DSA + Software Engineering fundamentals or continue with Data Science/AI?
6. What salary range is realistically achievable for a good fresher if I prepare seriously?

I know there isn't a completely "secure" job in IT, but I'm looking for the best risk/reward option rather than chasing whatever is trending right now.

Would really appreciate honest opinions, especially from people who are already working in these fields.

Thanks!

Hey everyone,

I'm currently exploring my career options and have been thinking seriously about going into Software Engineering, particularly backend/software development.

I keep seeing very mixed opinions about the current job market. Some people say software engineering is still one of the safest and highest-paying tech careers, while others say the market is becoming extremely competitive because of layoffs and AI tools.

I'm trying to get a realistic picture from people who are actually working in the industry.

A few things I'm curious about:

• How is the job market for software engineers in 2026, especially for freshers?
• Are companies still hiring fresh graduates, or has the entry-level market become much harder?
• What skills are currently most important for getting a good SWE job?
• Is DSA + CS fundamentals + backend development still a good combination?
• How much impact are AI coding tools actually having on SWE jobs?
• Is it better to specialize in backend/distributed systems, or should someone also learn AI/ML?
• For someone targeting around ₹15 LPA as a fresher in India, is that realistic?
• What would you recommend learning over the next 12–18 months to become genuinely job-ready?

I'm not looking for the usual "learn to code and you'll be fine" advice 😅. I'd really appreciate honest opinions from people who are currently working as software engineers, hiring managers, or recent graduates who have gone through the process.

Thanks!

I’m currently learning Data Analytics and Data Science and I’m trying to figure out which certifications/courses are actually worth doing.

There are so many courses out there, especially on Coursera, Udemy, Google, IBM, Microsoft, Kaggle, freeCodeCamp, etc., that I’m honestly a bit confused about where to start.

I’m mainly looking for free courses (or very cheap ones) that are actually useful for learning and also give a certificate that is at least somewhat valuable on a resume.

I’m interested in topics like:

• Python
• SQL
• Excel
• Power BI / Tableau
• Statistics
• Data Visualization
• Machine Learning

So, for those of you already working or learning in Data Analytics/Data Science:

What free courses or certifications would you genuinely recommend?

And if a particular certification helped you get an interview or land a job, I’d really like to hear about that too.

Thanks in advance! 😊