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Thank you for sharing!!

sancho pancro

Ah, I got most of these in my interview at a fintech startup for an Intern position last Autumn Anyways got rejected as I couldn't code up a snake-ladders-simulator after 40-45mins of rapid fire ML fundamentals 😭💀😬 That was my first and last interview, haven't really got any post that. Thanks for sharing

If I became good in python can I move forward toward machine learning engineering? This job will be important for the future?

Can we export plotly video as.mp4?

Hey, love this video thank you! I noticed at 3 minute 25 seconds the diagram shown has "ML entrepreneur." I have been digging into entrepreneurship/ ML for some time and would like to ask in your day to day roles do you work with external ML solution providers/ entrepreneurs? And what that route would look like. I've always been self employed so really just trying to see if theres a route for ML professionals to be self employed as opposed to working in corporate. Your reply would be much appreciated!

It’s amazing how this video: What professional software engineers ACTUALLY do, ruclips.net/video/Q0A35ZfgwHA/видео.htmlsi=1GUqy1dK-dzVuHX0 talks about the exact same thing. Exact same words, exact same points … I’m not sure who copied who, but how about being just a tiny bit more creative with the copied content ? 🤦🏾‍♀️

Came to this video right after wasting 3min on another video just like you described in the beginning. Thank you!

Ok, interesting video. But after learning how tinyml behaves, what does it do and how easy is it to program and adapt to my needs?

Hey Sandro, I am looking for a piece of advice. I am a mid level automation QAE in a large company. I want to believe I am pretty technical and in my role I have a lot to do with coding. I’ve been working on transitioning into SDET role, but with AI becoming a the biggest deal in AI I am considering taking a ML bootcamp and trying to land a role as ML SDE. I don’t have a computer science degree and learned everything myself and by working. Do you think it’s realistic to get an ML Engineers role with my background after taking a comprehensive bootcamp?

So tough to break into ML tried to land a job for over a year now just given up on it 😔

That production was amazing in the beginning 😂😂😂🔥🔥🔥

Can you please share the ipynb file

Took ML course at my uni and failed the exam because it was all about proving formulas by hand (bruh). Why do unis overcomplicate everything :(

I think the problem is trying to find a course that teaches like this. I've started many courses labelled as suitable for people with high school level math education, only to find after a few hours, things are being explained to me using massive equations with meaningless symbols that look like they belong on the predators watch 😂 I'm already a decent python programmer with my own successful commercial software product, but it's been quite frustrating trying to find a suitable learning path so far. Just need a course that teaches how to build models first, before moving on to how it all works under the hood, otherwise I'm learning all the small details without knowing how to apply them. So far it's been like a driving instructor teaching me to drive by explaining the chemical composition of each car part.

Thanks. By the way, the audio is very quiet, and also some of your words completely disappear. Your noise gate threshold is probably set higher than your quietest words 👍

I'm getting my BS in data science currently and will be moving on to get my masters in machine learning. I'm not doing it for all the "fun things" in fact I would dread having to do those things that people consider fun. I'm pursuing this path because I want to sit behind a desk for 6 hours a day (preferably remote work from home) because I've had jobs where I had to wake up at 3 in the morning drag myself to work just to work in the rain at 15 degrees Fahrenheit.

10th Class Maths Is Enough

Please I have a question I’m a student and I don’t know what career path to choose I don’t know if I go to SWE,cybersecurity or ML and I’m actually a data science student please how you doing to choose your path ?

Do you mentor people? Like one on one mentoring to help those wanting to get into the field?

great¡¡¡ could you recommend some courses for free on internet plz?

This was pretty helpful, tanks

you need to know the names of esoteric equations, not necessarily how you use them. Chances are most of the guys in the field have very little real math skills, most of the math happens when you are shitting out academic papers and when trying to sound smart in discussions.

update it for 2024 then, plz. Worth to become ML engineer in 2024?

What is the value of even trying to solve these real-life problems if, most of the time, there is no solution, no correlation? If my work is not being used, will I risk getting fired?

1:01: 📚 Don't be intimidated by math in machine learning, as you can get away with doing the bare minimum. 4:38: 📚 Understanding statistics, probability theory, and linear algebra is crucial for machine learning. 7:25: 🧮 Learning mathematics in machine learning is important, but focus on the basics and have fun building models. Recap by Tammy AI

A very true video... Really appreciate it 👍

What if my data is in hundreds of csv files...?

awesome video sandro ... love it

loving the 'slightly more honest' quip Sandro!

1- Bias-variance tradeoff 2- Difference between training set and validation set 3- Interpretability vs Accuracy 4- SQL questions 5- Behavioral questions 6- Approaching a problem with no labels 7- If you had an API how would you load a csv? 8- What is pep8? 9- What is a confusion matrix (error matrix)? 10- Anomaly detection 11- Grid search 12- Coding challenges

Hi Maestro, hope you are doing well. I just wanted to ask, if I do masters in computer science...can I have a prospect of becoming ML engineer in the future? P.S. I believe that without becoming a good software engineer, I can't become a good ML engineer. So asked.

Hello im currently on my last year of bachelor in cs .i m goodan pythonand its few ml libraries should i focus on learning math and ow to build model or sql, data cleaning andhow to use model please help 1:58

Its 2023 and its still a great video for me starting AI journey

``` #include<map> #include<iostream> #include <string> using namespace std; int main() { map<float, string>FloatString; FloatString[42] = "dog"; FloatString[42.0] = "cat"; for (auto element : FloatString){ cout<<element.first<<" is the capital of "; cout<<element.second<<endl; } return 0; } ``` Test this code in cpp and you will feel cpp is also strange.

Posted A year ago lol. Do you remember making this

Isn't AI engineer and ML engineer seperat major or ther is like mix of them

This is a great video! I am just starting my bachelor at WGU and venturing into understanding the world of IT/AI. I am not sure which of the degrees offered by WGU best suit the machine learning engineer goal; software engineering or computer science? Do you have thoughts on this? Any additional items I should focus on outside of the bachelor to ensure employability?

damn, desk setup tours be like🤣😂😂

Hey Sandro!, i just want to thank you for posting vids about ml. You really inspire me and really showed me guidance.❤

and what about NLP engineer? I’m actually thinking to go for a master degree in computational linguistics and language technologies and I’m actually getting very interested in NLP and help machine’s understand language, I know there are many linguist that work as NLP engineers, and also I even saw many job announcements about ML/NLP engineer that were asking also for a degree in computational linguistics ( others even a PhD in math so I think it really depends ). But I’m not sure if this is a path meant for linguists or not. Do you think NLP is a job that you can do only if you come from a STEM education with solid knowledge of math -algorithms? Obviously I’m not referring to ML/NLP researchers that research and create new algorithms and models from scratch, that’s not even what I want to do actually, but I mean to be an “applied NLP engineer “. Ppl told me you actually don’t need much math if you don’t want to create new algorithms, you just need some knowledge of calculus and linear algebra ( and more stronger knowledge of statistics) because nowadays you just use PyTorch or similar, but I don’t know until what point it is true I mean, for sure I know that I can’t take a ML university class in my degree since it requires too much math since it’s the classic theoretical academic course, so I’ll learn ML in other ways

Hi sandro. Thanks for this tutorial. I found it very easy to follow. However, I'm not sure if I correctly understood what your aim was when using the set_fold method. I wish you could give us more examples about this. Thanks!!!

Sandrooooo… where are you brother? Come out of RUclips retirement! Hope everything is okay

Damn! You went to ETH & UZH holy shit! No wonder you are good at ML

Almost 4 minutes without answering the question, I’d recommend that you just go straight to the point or people will lose interest.

I heard from a recruiter you should never do the bars for experience. Like what are you actually quantifying? What are the units? haha. How much you're not good at something? How much you're unqualified for the role? If you're not good at something, then why are you listing it on an application?

Sir..! You are really a superb guy..❤ I really love the way you explain as you have an indepth knowledge in ML. I love ML and your channel as well.. 💕💕💕. Thank you for such great content🤝🤝

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00:00 Most videos about the life of a machine learning engineer show only the highlights, but the reality is that they spend most of their time behind a desk, working on a computer. 00:59 A typical day for a machine learning engineer involves a morning stand-up meeting with the team, discussing problems and plans for the day. There may also be additional discussions and collaboration with team members on infrastructure and project-related issues. 03:27 The day is usually divided between meetings and coding, with meetings involving discussions with stakeholders about model performance, technical discussions about infrastructure, and planning for future projects. 04:53 The job titles in the field of data can vary greatly, and it's important to understand the different roles and levels of variation within a company before joining. 05:23 The distribution of tasks in a machine learning engineer's day can vary, with a mix of machine learning project work, infrastructure work, support, and meetings. These proportions can fluctuate depending on the stage of a project or other factors. 06:22 Machine learning engineers often work on the same type of ML problem for a long time, focusing on a specific domain or use case. They also work on someone else's code base and tend to settle on specific tools and technologies. 07:51 Documentation is an important part of the job, including writing documentation in Confluence, writing emails and messages, and documenting progress and decisions in JIRA. 08:51 Real-world data is often messy and noisy, requiring a lot of data cleaning and dealing with unexpected issues. Finding clear correlations and solutions can be challenging, and many projects may not have the desired outcome. 10:19 Despite the challenges, being a machine learning engineer is still considered one of the best jobs in the world, but it's important to acknowledge the imperfections and not pretend that everything is perfect.

Great rundown