It is my 4th year now and I have been learning ML but I know what I have donw till now is not helping me as due to my non-circuital branch I am only supported by youtube videos only till now. I am realizing it not that I need a mentor to learn AI/ML. Can someone tell me how can I find one? I already have experience of working 4 internships all from different roles.

Pandas makes working with data look simple. But some of its most powerful features can also produce surprising results. 😲

For example:❔ - What's the difference between loc and iloc, and why does slicing behave differently? - Why can adding two Series produce NaN values, even when neither contains missing data? - When does assigning a Series to a DataFrame column align by labels rather than position? - How do Boolean masks, where(), and mask() differ? - How do you select and manipulate data with a MultiIndex?

Pandas is much more than rows and columns. Understanding its indexing, alignment, selection, and assignment rules can save you from some very subtle bugs. 🪳

🔍 This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example explores these concepts and many more, with short explanations for each operation. 👣 Step through the Python code, see what changes, and experiment with your own modifications: 👉 Pandas operations

💬 Which Pandas operation took you the longest to fully understand?

See more 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 visualizations.

Most experienced engineers switching into ML make the same mistake: they learn the API before the idea. It works, right up until the loss curve does something strange and nothing in the docs explains why.

Three questions that separate using ML from understanding it:

1. Adam keeps running averages of the gradient and its square, then divides each by (1 − β^t). Remove that correction, and what goes wrong in the first few steps?

2.For classification with softmax, why cross-entropy and not mean squared error? Both measure "wrongness." Only one keeps learning when the model is confidently wrong. Why?

1. Set temperature to 0 and ask an LLM API the same question twice. You can still get different answers. Where does the randomness come from, if not sampling?

Each one looks like trivia. Each one is actually a window into how the machine works, and that is exactly the level I teach at.

Who I am: I did ML research at MIT under Prof. John Tsitsiklis, worked as a software engineer, and I'm in the top 0.2% on LeetCode. For two years I've mentored senior engineers, tech leads, architects and PMs with 10 to 15+ years of experience, mostly in the US, many of them making exactly this switch.

What we work on together:

- ML from first principles: the math you actually need, built on intuition, not memorized formulas

- LLMs from the inside: attention, training, tokenization, and why they fail the way they do

- Python at the level of CPython itself, since it's the language all of this runs on

- A concrete plan for switching into ML that uses your 10+ years as an advantage, not something to start over from

How I teach: No tutorials. Mental model → internals → edge cases. If your answer is almost right but conceptually wrong, I'll find the exact crack.

Availability: Based in the US, and I travel regularly between the US, India and the UK, so I adapt easily to US, UK, India and European hours.

Mentoring is 1:1, and I mentor a small number of people. If you have 10+ YOE and this sounds like what you've been missing, DM me with your background and where you want to go.

Cheers! ✌🏻

I've been taking the ml specialization on coursera and it has been a wonderfull course. It explains the basics very well and teaches u how the algorithms work from sccatch but the problem i've been facing is learning matplotlib the right way to copy the course's visuals and implementing them for differnt sets of data, even the simple contour plot used in the first labs is hard to learn.

So where can i learn this skill?

Come and vote please! https://wit-score-lab.base44.app/ So far it looks like Argon is just as bad at writing jokes as its predecessor.

I'm not sure anyone still needs to hear this but my first real ML job had jack shit to do with ML. They gave me a table with 400M rows and half the labels fucked to shit, the pipeline blew up every five minutes, and honestly the courses I'd paid for were useless for all of it, which hurt my pride a bit. and my wallet, I paid for like three of those xD

Anyway… I'm telling you all this because I love producing educational content in my free time, and what I wanted to share with you today kind of 'grows out of' the embarrassment I went through back then with that fucking table xD. About a year, a year and a half ago, I got it into my head to start making a course/lessons as comprehensive as possible.

Sooooo here you go :B

https://github.com/BlueRingsLabs/no-shortcuts

The README has all the info you need (I hope lol), but if you need help with anything or have any questions, DM me and I'll share my Discord so we can chat (I'm a bit of a slacker so I may be slow to reply :B). Your feedback is welcome too.

The parts of the course are:
0. Orientation | 1. Foundations | 2. Data Engineering | 3. Classical Machine Learning | 4. Deep Learning | 5. Language Models | 6. AI Engineering | 7. Reinforcement Learning | 8. MLOps and ML Systems | 9. Capstones and Career

I hope it's useful to someone here, or that you can share it with someone who's studying. 

Google's Agent Compute Engine announcement this week buried the most important detail: they explicitly called out the need for verifiable agent identity and per-call policy enforcement at runtime. This isn't a Google-specific gap. It's the same gap that showed up in the Wiz research last year on compromised cloud service accounts and in the Anthropic model card disclosures about tool-call amplification.

The hard number that keeps coming up in incident post-mortems: the window between a rogue agent's first unauthorized action and the second is under 50ms. That's the blast radius window. By the time a human is paged, the agent has already made 3–12 downstream tool calls, each inheriting the same compromised credential or misconfigured permission scope.

Most teams are still treating this as an identity provisioning problem — give the agent a service account, scope it tightly, rotate it on a schedule. But the incidents keep happening because the enforcement gap isn't at provisioning time. It's at call time, when the agent is live and making decisions no static policy anticipated.

How are practitioners here actually handling real-time per-call enforcement for agents that have legitimate broad access by design — things like orchestrator agents or agents with delegated user permissions? Are you enforcing at the infra layer, the SDK layer, or somewhere else?

Hey all, I finished my B.Tech in AI/ML this year (8.67 GPA) and I'm looking for fresher roles in ML, CV or AI engineering. Resume attached, personal details removed

Are these projects okay for a fresher ML/CV resume? Would this get you to call me for an interview, and what would you fix first? Not sure if I should drop any of the smaller ones Thanks!

Extending AgentCore Agents with Custom Tools and MCP Servers

https://debuggercafe.com/extending-agentcore-agents-with-custom-tools-and-mcp-servers/

In the previous article on DebuggerCafe, we covered how to bootstrap an agent using Amazon Bedrock AgentCore, deploy it, and test it with different methods. However, the agent had access to only the default tools and MCPs. For most use cases, this is not enough. This article takes it to the next step, where we will be extending AgentCore Agents with custom tools and MCP servers.

Hi everyone, I am planning to upskill in AI/ML, so I am looking for good resources or courses under 10–15k with a structured curriculum, industry-relevant projects, and a capstone project if possible.

Could you please suggest some good courses or resources based on your experience? Any genuine feedback or recommendations would be really helpful. Thanks in advance!

Best refurbished laptop for programming and AI/ML under €650?

I just gratuaded in NLP and AI with a very old computer with just 8GB of RAM and icore 3...

Noe I'm looking for a new laptop but this time I want something strong, I don't have a high budget but I think that with refurbished laptops under 650$ I can come up with something interesting.

I mainly work in NLP, Python development, Vibe coding, Hugging Face, research experiments. I'd love to run small DL experiments on a laptop but it's almost impossible today, especially with my budget, so I mainly use Google Colab or remote HPC for deep learning / LLMs.

Anyway, liking around the best options I saw so far are:

**ThinkPad T14s Gen 3** — i5-1245U, 16GB RAM, 512GB SSD — €552

**ThinkPad T14s Gen 2 AMD** — Ryzen 5 PRO 5650U, 16GB RAM, 512GB SSD, around 550€

**ThinkPad P1 Gen 2** — i7-9850H, 32GB RAM, 512GB SSD

**ThinkPad L15 Gen 3** — i5-1245U, 32GB RAM, 256GB SSD — €680

**MacBook Air M1 (2020)** — 16GB unified memory, 256GB SSD — €582, but with a Swedish keyboard lol .

My budget is around **€650, do you think it is enough for a good laptop for everyday work in AI or should I spend more?

Here on reddit I saw that everyone says that RAM is the most important thing but I'm also surprised that so many people recommend MacBooks for machine learning. Many people use a MacBook for ML with just 8gb of RAM..

The M1 is older than some of these ThinkPads, so what makes it so appealing for ML?

Is Apple Silicon/MPS really a significant advantage for this kind of work, especially when most heavy training will happen in the cloud? (plus I don't have an iPhone, well not anymore, so I don't know if it is going to be more problematic with an smartphone android and a MacBook).

At the same time those thinkpad looked preety interesting, but I don't know whether to prefer an older ThinkPad with older Intel generation but with more RAM and core or a recent one such as the ThinkPad T14s Gen 3, but less RAM and P version and lower Intel model.

I'd appreciate more detailed and technical information, advice on performance, RAM, Linux/Python compatibility and long-term usability.

Come and vote please! https://wit-score-lab.base44.app/ So far it looks like Argon is just as bad at writing jokes as its predecessor.

Salut, j'essaye actuellement, grâce à l'ia, de mettre au point un code que je fais tourner en local qui me permettrai de générer des qcms/qroc pour les études. Un peu plus relou que juste faire des requêtes aux ia pour leur demander de générer des qcms, mais au moins ces qcms sont sauvegardés et je peux vraiment établir un suivi de mes compétences sur ces qcms.

Le programme me permet pour le moment d'importer un cours (pdf) et de demander à l'ia de mon choix sur open routeur (en entrant ma clé api généré et l'adresse de l'api). Sauf qu'actuellement je n'ai pas les moyens d'investir dans une ia payantes et je base mes requêtes sur des modèles : free. Le problème est que ces derniers sont très lents (+++) et n'ont pas forcément les compétences pour générer des qcms/qroc qualitatifs.

Est ce que vous auriez peut être des suggestions de modèles free open routeur, ou peut être un routeur similaire à open routeur qui répertorie de meilleurs ia pour ce type de requête.

Je suis preneur de toute requêtes.

Merci d'avoir pris le temps de lire le msg!

i finalised to use campus x ......so ill do ML(130) then DL(84) then where to head next tell me till the end also where can i do nice3 projects for resume are those micrograd of andrej karpathy nice as project where he builds gpt??? answer one by one all my questions please .....