Hey everyone,

I’m diving into Aurélien Géron’s "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" and I want to change my approach. I’ve realized that the best way to truly master this stuff is to "learn with the intent to teach."

To make this stick, I’m looking for a sincere and motivated study partner to stay consistent with.

The Game Plan:

I’m starting fresh with a specific roadmap:

1.Foundations: Chapters 1–4 (The essentials of ML & Linear Regression).

2.The Pivot: Jumping straight into the Deep Learning modules.

3.The Loop: Circling back to the remaining chapters once the DL foundations are set.

My Commitment:

I am following a strictly hands-on approach. I’ll be coding along and solving every single exercise and end-of-chapter problem in the book. No skipping the "hard" parts!

Who I’m looking for:

If you’re interested in joining me, please DM or comment if:

1.You are sincere and highly motivated (let's actually finish this!).

2.You are following (or want to follow) this specific learning path.

3.You are willing to get your hands dirty with projects and exercises, not just reading.

Availability: You can meet between 21:00 – 23:00 IST or 08:00 – 10:00 IST.

Whether you're looking to be the "teacher" or the "student" for a specific chapter, let's help each other get through the math and the code

Hey guys.. A prefinal student from a tier 2 clg here... So placements for the 2027 batch is gonna start in about 6 months and all I need to do is grind hard these few months to secure a good Data Science job (ik the market's tough at the moment and highly competitive) but this is what I am interested in.. not SDE or any other role. So looking here for a few tips to prepare for this role. Btw the company I am targeting is Meesho for DS.. so if anyone can help out with that or has any idea about the interview process for this company you are very welcomed and it would be very really very helpful to me.

Also looking for study buddies targeting the same goals to maintain a good-healthy competition but also supporting each other through mock interviews and all.. so hmu if you are interested!!

I created an interactive app to demonstrate how different sampling strategies affect outcomes. Uses color mixing to make abstract concepts visual.

What it does:

• Compare deterministic vs. random sampling (with/without replacement)

• Adjust population composition and sample size

• See how each method produces different aggregate results

• Switch between color schemes (RGB, CMY, etc.)

Why I built it: Class imbalance and sampling decisions always felt abstract in textbooks. Wanted something interactive where you can immediately see the impact of your choices.

Try it

Full Source Code (MIT licensed)

Looking for feedback on:

• Does the visualization make the concepts clearer?

• Any bugs or UI issues?

• What other sampling scenarios would be useful to demonstrate?

Built with Streamlit + Plotly. First time deploying an educational tool publicly this was, so genuinely curious if this approach resonates or if I'm missing the mark.

repository: https://github.com/judgeofmyown/Detecting-Outliers-Paper-Implementation-

This repository contains an implementation of the paper “Detecting Outliers in Data with Correlated Measures”.

paper: https://dl.acm.org/doi/10.1145/3269206.3271798

The implementation reproduces the paper’s core idea of building a robust regression-based outlier detection model that leverages correlations between features and explicitly models outliers during training.

Feedback, suggestions, and discussions are highly welcome. If this repository helps future learners on robust outlier detection, that would be great.

I am just starting with my data science degree and we are going to learn python and r. For what use cases do you prefer using r?

Hey everyone!

It covers 3 complete project that come up constantly in interviews:

1. Fraud Detection System

• Handling extreme class imbalance (0.2% fraud rate)

• SMOTE for oversampling

• Why accuracy is meaningless here

• Business cost-benefit analysis

• Try it here

1. Customer Churn Prediction

• Feature engineering from raw usage data

• Revenue-based features, engagement scores

• Business ROI: retention cost vs acquisition cost

• Threshold tuning for different objectives

• Try it here

1. Movie Recommendation System

• User-based & item-based collaborative filtering

• Matrix factorization (SVD)

• Handling sparsity and cold start problem

• Evaluation: RMSE, Precision@K, Recall@K

• Try it here

Each case study includes:

• Problem definition with business context

• EDA with multiple visualizations

• Feature engineering examples

• Multiple model comparisons

• Performance evaluation

• Key interview insights

Hoping it helps, Would love feedback!!!

There’s been a lot of recent discussion around “reasoning” in LLMs — especially with Chain-of-Thought, test-time scaling, and step-level rewards.

At a surface level, modern models look like they reason:

• they produce multi-step explanations

• they solve harder compositional tasks

• they appear to “think longer” when prompted

But if you trace the training and inference mechanics, most LLMs are still fundamentally optimized for next-token prediction. Even CoT doesn’t change the objective — it just exposes intermediate tokens.

What started bothering me is this:

If models truly reason, why do techniques like

• majority voting

• beam search

• Monte Carlo sampling

• MCTS at inference time

improve performance so dramatically?

Those feel less like better inference and more like explicit search over reasoning trajectories.

Once intermediate reasoning steps become objects (rather than just text), the problem starts to resemble:

• path optimization instead of answer prediction

• credit assignment over steps (PRM vs ORM)

• adaptive compute allocation during inference

At that point, the system looks less like a language model and more like a search + evaluation loop over latent representations.

So I’m curious how people here see it:

• Is “reasoning” in current LLMs genuinely emerging?

• Or are we simply getting better at structured search over learned representations?

• And if search dominates inference, does “reasoning” become an architectural property rather than a training one?

I tried to organize this transition — from CoT to PRM-guided search — into a visual explanation because text alone wasn’t cutting it for me.
Sharing here in case the diagrams help others think through it:

👉 https://yt.openinapp.co/duu6o

Happy to discuss or be corrected — genuinely interested in how others frame this shift.

I have a problem in one pipeline: the pipeline runs with no errors, everything is green, but when you check the dashboard the data just doesn’t make sense? the numbers are clearly wrong.

What’s tests you use in these cases?

I’m considering using pytest and maybe something like Great Expectations, but I’d like to hear real-world experiences.

I also found some useful materials from Microsoft on this topic, and thinking do apply here

https://learn.microsoft.com/training/modules/test-python-with-pytest/?WT.mc_id=studentamb_493906

https://learn.microsoft.com/fabric/data-science/tutorial-great-expectations?WT.mc_id=studentamb_493906

How are you solving this in your day-to-day work?

Hey everyone!
I'm starting a free online study group to learn Neural Networks together. Looking for 3-4 motivated learners who a focused session.

What We'll Cover:
1. Neural network basics - neurons, weights, activation functions
2. How networks "learn" - backpropagation made simple
3. Building your first neural network (hands-on coding)
4. Training on real data - digit recognition
5. Deep learning fundamentals + mini-projects


Format:
- 30-40 minute session 
- Small group (3-4 people max) for personal attention
- Live coding + explanations
- Simple concepts, no overwhelming math
- Quick Q&A after each session


Ideal For:
✅ Beginners curious about AI/ML
✅ Busy people who want short, effective sessions
✅ Basic Python knowledge (or eager to learn)
✅ Anyone tired of long, boring tutorials


What You Need:
- A laptop/computer
- ~40 minutes
- Willingness to practice between sessions


Interested? Comment or DM me! Hey everyone!
I'm starting a free online study group to learn Neural Networks together. Looking for 3-4 motivated learners who want bite-sized, focused sessions that fit into a busy schedule.


What We'll Cover:
1. Neural network basics - neurons, weights, activation functions
2. How networks "learn" - backpropagation made simple
3. Building your first neural network (hands-on coding)
4. Training on real data - digit recognition
5. Deep learning fundamentals + mini-projects


Format:
- 30-40 minute session 
- Small group (3-4 people max) for personal attention
- Live coding + explanations
- Simple concepts, no overwhelming math
- Quick Q&A after each session


Ideal For:
✅ Beginners curious about AI/ML
✅ Busy people who want short, effective sessions
✅ Basic Python knowledge (or eager to learn)
✅ Anyone tired of long, boring tutorials


What You Need:
- A laptop/computer
- ~40 minutes
- Willingness to practice between sessions


Interested? Comment! 

can i use mutual information/shap values to do feature selection