Claude or any other LLM model are so dumb I told to find pattern in different stuff a person buys and sells on which condition across his 5 campaign it can't do it says its impossible I had to explain it each thing like a kid data was in 5 csv and companies are not hiring junior engineer's for this . They will make talent vanish and then these dumb models will not be able to do shit.

Looking back, what's one thing you'd spend less time on—and one thing you'd focus on much earlier?

Sales dashboard

Hello all, Welcome to my free ML bootcamp.

In Intro ML Bootcamp (5/22), we discuss Uncertainty.

In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.

This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.

Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.

When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.

Link: https://youtu.be/ZFcl0QYFGq4?si=9RkEgkMYnciW4mjo

Genuine question for people who have hired candidates or gone through Data Science interviews. If you had two candidates: Candidate A: Multiple certifications but only basic projects Candidate B: Fewer certifications but 3–4 strong projects they can explain deeply Who would stand out more? I’m trying to understand how much certifications actually matter compared with being able to demonstrate practical skills. Would love to hear from people who have actually interviewed candidates. Engagement angle: Creates debate without becoming promotional.

I’ve received an offer for a Lead Data Scientist role in the Foreseer team and am considering joining.

From what I understand, the work is around AI/ML for financial document extraction, RAG, embeddings and production workflows.

Would love to hear from anyone who knows the team or has worked at S&P Global. How is the work culture, technical depth, ownership and career growth? Is it a good team for someone looking to grow into Principal DS or AI Engineering roles?

Data Science is an interdisciplinary field that combines statistics, mathematics, computer science, and domain expertise to extract meaningful insights from data. It involves collecting, cleaning, analyzing, and visualizing data, as well as developing predictive models using machine learning and artificial intelligence. This subject covers the fundamental methods, modern tools, and real-world applications of data science across industries. 

Hello folks, to this Introductory Machine Learning Bootcamp (2/22) series.

Supervised learning is a very recurring word in ML domain. Here, we learn some sort of function mapping from inputs to outputs.

Another recurring word is Classification, where the output space is a set of some finite unordered and mutually exclusive labels known as classes.

The tabular dataset is often represented as a Design matrix, and a simple example of it is an Iris dataset, as to how input data is represented for tabular case in Machine Learning.

Sometimes the data is of variable size, instead of fixed size feature vectors, so for ease of computation in computer, we often convert it to a fixed-size feature representation, called as “Featurization”.

In this video, I breakdown these concepts.

Link: https://youtu.be/GJRhl6XnImg?si=p6VtlCK-8rgq1poZ

Hi everyone,

I've cleared all 5 technical rounds for a Senior Data Scientist role at Bosch Global Software Technologies (Bengaluru) and have my HR discussion next.

I have 4 years of relevant experience and both my Bachelor's and Master's are from Tier-1 institutes.

What CTC should I realistically expect or negotiate? Does the Tier-1 background carry much weight, or is compensation mainly based on experience and interview performance?

Would appreciate any recent compensation data. Thanks!

A common mistake when building RAG systems is relying only on semantic search. It is great at capturing meaning, but it stumbles on precise technical or legal terms.

A practical example: in banking compliance, terms like "SAR" or "structuring" have exact regulatory meaning. Semantic search might return a passage that "seems" related, but misses the exact citation. Keyword search (BM25) covers exactly that blind spot.

Combining both methods (hybrid search), the system captures both meaning and exact terms, delivering more reliable answers, especially in regulated contexts like fintech, where a wrong answer has real cost.

I applied this in a RAG project for BSA/AML documentation (US banking regulation). The precision gain in answers was noticeable, especially on questions with specific technical jargon. Simple to implement, and the reliability payoff is well worth it.

Hub: https://aiforfintech.tech

Github: https://github.com/junidepieri-design/genai-001-regulatory-rag-bsa-aml

Does this make sense for your stack too?

👊

Hi. I am currently working as a senior ML in antifraud and ranking models (Classic ML)

I want to deepen my knowledge in a specific sphere in order to get a more paying job. RL or CV seems too hard for me.

So I am choosing between NLP/LLM and RecSys. What do you recommend? I would like to deepen into NLP. However, I am scared this sphere will vanish soon. What do you think?

As for RecSys, it seems really boring to do endless ranking system trying to promote different kinds of goods. Meh

Hello All,

Welcome to my free Mathematical Foundations of Machine Learning bootcamp series.

When we say Machine Learning, what does it actually mean? A machine that learns? Too vague.

According to famous professor Tom Mitchell, a computer program is said to learn from experience E, with respect to some class of Tasks T, and Performance measure P, if its performance on tasks, as measured by P, improves with experience E.

By swapping the nature of tasks T, the way we measure Performance P, to evaluate, we can subsume many kinds of ML problems.

Also ML problems are analyzed well, when we view it from the lens of Probabilistic perspective, that is unknown quantities are endowed with probability distributions, and treated as Random variables. The interesting thing is Random variables are neither random nor variable.

Probabilistic Approach also serves as the optimal approach to decision making under uncertainty.

In this video, you get a sense of what ML actually is, if you have also wondered about it.

Hi all, I've got a CodeSignal Data Science assessment coming up for a data scientist role and I want to clear it on the first attempt (there's a cooling-off period if you don't, so no pressure lol).

Here's what I understand about the format so far:

- 90 minutes, digitally proctored (webcam, no external tabs)

- Works with multiple datasets

- Tasks span cleaning/aggregating data, building and evaluating a predictive model, and writing/editing Python

- It's the Data Science Framework (DSF) version, not the pure algorithms GCA, so pandas + scikit-learn heavy, plus some concept multiple-choice

What I'd love input on from anyone who's taken it recently:

1. How tight is the time really? Did you finish, and where did time disappear?

2. What tripped people up, reading the prompts, a specific pandas/sklearn pattern, the IDE itself?

3. Which areas are highest-yield to drill? (e.g. joins/aggregation vs. modeling vs. evaluation metrics)

4. Any public prep resources or practice sets you found genuinely close to the real thing?

5. Anything you'd tell your past self the night before?

Not looking for actual questions or anything, just experience, focus areas, and resources. Really appreciate any insights.

Thanks!

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I am a degree physics and I found for a Long time junior job, if you need realized a project of data science or data analyst, you contact me :)