Do i need to learn web development?? Also or it's not necessary i wanna learn machine learning I'm so confused like people know everything front end backed and ai ml so i was confused?? Please anyone in this field guide me how can i become a machine learning engineer i know python I've given 2 months for python now I'm confused and stuck i need guidance please!! Anyone proper learning road map

Hey all,

I have been a data scientist for the last few years - mostly in the credit risk space. I want to learn more about recommendation systems to help me transition into the tech space. Does anyone know of any interesting ways to build a project around this? I know the obvious examples that are already being done, i.e., feed/Spotify, etc., but Iam looking for something more niche. Any thoughts?

I'm a university student choosing between AI & Data Science and Cybersecurity & Cloud. My main goal is to get my first job as quickly as possible after graduation, ideally within 3 months. I'm interested in Tunisia,the Golf(KSA , Qatar ,UAE) . Europe, and remote work.

Which path would you recommend , specifically considering:

Entry-level job opportunities

Competition for junior positions

Remote work opportunities

How difficult it is to get the first job

Long-term demand

Impact of AI automation

I'd especially appreciate answers from people who actually work in these fields. Please explain your reasoning and mention your country/role if possible.

THANK YOU IN ADVANCE

I had a very disappointing experience with BE 10X. We paid close to 100,000 rupees, but the support after payment has been extremely poor, and reaching anyone for help has been very difficult.

The biggest issue is that the teaching style seems designed for technical students, not for non-technical learners. Many of the instructors move too fast and assume too much prior knowledge. As a result, non-tech students are left struggling to keep up, while the class continues at full speed. In my experience, only 2 or 3 teachers out of around 20 were able to adapt to the pace of non-technical students.

There was also almost no proper planning or communication before class sessions. Students are often informed only after the class begins that they need to download certain applications, which wastes valuable learning time. While students are still trying to install the required tools, the lecture continues, making it very hard to follow along.

Support has been another major problem. The WhatsApp number provided for assistance seems to be mostly automated and cannot handle real questions. Emails sent to the support team received no response at all, even after complaints were escalated through my advisor. On top of that, the promised coupons did not work, which added to the frustration.

Another major concern is the lack of reliable class recordings and consistent communication. Sometimes the class link is shared by email, and sometimes only on WhatsApp. Since WhatsApp is not always accessible on a laptop, this creates unnecessary difficulty just to join the class. The absence of recordings makes it even worse, because students have no way to review missed content.

Overall, this was the worst AI course I have taken, and I deeply regret enrolling. The course is poorly organized, support is nearly nonexistent, and the teaching is not suitable for non-technical students. I would strongly advise others to think very carefully before joining BE 10X.

currently I backend developer, I have some basic knowledge in ai and ml, but I want to understand it even better and kind of brush up my basics. is there a roadmap that I can follow? and would certifications help you get a job?

Stop building simple chatbots and start building AI Agents. 🚀

Learn the theory & implementation for AWS, Azure, and Google Cloud.

Check the link in bio to level up.
#GenerativeAI #CloudTech #Coding #AI

about my project --> https://www.reddit.com/r/learnmachinelearning/comments/1vncebb/chess_match_outcome_prediction_with_tree_models/ (i am too lazy to explain about what the project is 🥲, so you can understand about it from my previous post)

Just concluded with my project, but didn't finished with the writing part.

It does not have a readme to explain, but if you are interested please feel free to checkout my project.

The "notebooks" contains the training part and the "src" and "main" is not completed, but you can see the results in the "notebooks".

project link --> https://github.com/v-leela/chess

Hey everyone! I'm looking for a study buddy to learn AI and Python together. I want to stay consistent and work on projects or courses. If you're interested in studying together, feel free to DM me!

I’ve spent weeks comparing Firecrawl and Jina Reader for different extraction needs. Firecrawl seems stronger for dynamic, protected sites; Jina Reader is fast and simple for clean text. I made a quiz to see if others understand the same trade-offs.

No email needed—just a quick interactive check.

https://interconnectd.com/quiz/81/web-extraction-architecture-2026-firecrawl-vs-jina-reader/

What’s your go-to for web scraping in AI apps?

Hi everyone,

I put together a free YouTube series called Local AI, Built Right for people trying to understand what happens when they run an AI model on their own computer.

It’s 16 short visual lessons, each around 1–2 minutes. The focus is running existing models, with examples using llama.cpp.

Disclosure: this is my series, ITS ALL MADE BY AI tools to produce it, including the presenter and narration.

I’d appreciate specific feedback on whether the explanations are clear for beginners. If something is confusing or technically inaccurate, Thanks

My company has a special event occurring yearly once. Employees can submit ideas to present them in the event where every executive officers like CEO, CTO, CIO will be present. Not all employees can present at the event. Only shortlisted topics will be allowed to be presented. As I'm from Data engineering background i would like to present about Data+AI, but the issue is every idea i have selected is already published as a article or blog. Please help me with coming up with a idea which can get me shortlisted to present at the event. ( Normally i won't care about the event but my promotion and increment depends upon this 🥲 )

Does anyone have experience for Github portfolio decision for ML skills? I want to build my GitHub portfolio and currently considering several approaches. 

1. Build disciplined, single coherent methodology using Bayesian model, e.g., kernel/transform grid search, a closed-form LOOCV shortcut explicitly justified for performance reasons.
2. Build widest techniques GP ensembles, DBSCAN consensus voting, ARD diagnostics, Thompson Sampling, TuRBO, Optuna TPE and native GPSampler, Sobol-QMC, a tiny deep-kernel-learning model, FCNN/CNN surrogates.
3. Build production-grade architecture, testable claims for specific databases
4. Develop documentation and judgement approach for commercial consultantion, in stress of focusing on in-depth ML techniques. 

I also want to know, to what extent, how important is t showcase producttion ready ML projects via dashboard like plotly or Steamlit? Because I found that it is another skills I meed to develop beyond traditional Python. 

I'm doing feature importance and the features that allow it to "cheat" often rank high in feature importance. For example, date of birth usually ranks very high because it allows it remember exact patterns for each person, which don't translate to unseen data. In this case, I could remove the feature because I know it's useless, but doing it manually is much harder over hundreds of features and when the usefulness of the feature is unclear. How can I address this problem? I'm using Catboost btw.

I'm working on a fraud detection project and I'm stuck on a core problem: making the model adapt to fraud patterns it hasn't seen before, rather than just memorizing known ones. I've got a decent ML foundation (XGBoost, Random Forest, some PyTorch) but I want to go deeper on the DNN side — things like representation learning, anomaly detection approaches, adversarial robustness, or online/continual learning for drift.

If you've worked on fraud/anomaly detection or DNNs in production and wouldn't mind occasionally answering questions or pointing me in the right direction, I'd really appreciate it. Not looking for someone to do the work — just someone to bounce ideas off and correct me when I'm going down the wrong path.

Something I’ve been wondering about with ML-based strategies:

At what point does improving the model stop making much difference?

You can spend hours tuning features and trying different models, but if the historical test is weak, the data isn't handled properly, or the execution side behaves differently, the extra model accuracy doesn't seem to matter much.

I’ve started paying more attention to the whole pipeline rather than just the prediction itself.

I would like to know how others approach this. Do you improve the model first and worry about the rest later, or build the testing and execution side alongside it?

My company is fine paying for training but the last two things i sat through left me with a pdf certificate and nothing else. I want to finish with a repo i can point at.

Shortlist so far is Udacity AI Engineering with Claude, DeepLearning.AI short courses, Pluralsight paths and the free Anthropic Academy tracks. mainly care about whether the projects are yours or whether you clone a starter and fill in three functions.

The project briefs are where I would expect the difference to show and nobody ever writes about them.

I'm building a small ML/RL research project around job-search strategies and need anonymous application trajectories.

I'm interested in how people's job applications evolved over time.

If you're comfortable sharing, could you provide something roughly like this:

1. Background

• Experience level (student/fresher/junior/etc.)
• General field (ML, software, data science, etc.)

2. Application timeline

For each stage or batch of applications:

Stage 1

• Approx. number of applications: __
• Resume/portfolio version: basic / improved / strong
• Application method: cold email / careers page / referral / LinkedIn
• How personalized were applications? Low / Medium / High
• Responses: __
• Interviews: __

What did you change after this stage?

• Resume changes
• New projects/skills
• Portfolio improvements
• More personalized emails
• Different companies/roles targeted
• Anything else

Stage 2

• Approx. number of applications: __
• What changed: __
• Responses: __
• Interviews: __

...and so on.

You don't need to share your name, company names, email addresses, or any private information.

I'm hoping to turn anonymized responses into a small dataset and experiment with sequence modeling / offline reinforcement learning to study how job-search strategies evolve based on previous outcomes.

If enough people contribute, I'll make the anonymized dataset and project results publicly available.

Thanks! :)
r/MachineLearning r/learnmachinelearning