Vadim Timakin / ML&DS mentoring

The exact time and goal depend on the background, the chosen direction, and the grade. For example, in the case of the first job, we can reach the first offer of up to $5000 Net (and higher) in 6 months at a moderate pace or even faster, while upgrading to a new job with a higher salary may take only a few weeks.

As a starting point, you can take a 6‑month period and an offer of $4500 - $5000, with 15–20 hours of studying per week.

After the start, we can discuss the workload and a comfortable pace of learning, which can be adjusted at any time. I will also share all the knowledge about how to combine full‑time work and study; I’ve been doing this without any problems since my first year, and many of my students do the same.

The interaction doesn’t end there!

• The roadmap goes far beyond the basic topics for a first job.
• You will always have access to the roadmap.
• You will always have access to the community (as long as you don’t break the rules)
• You will be able to come to me for advice and ask questions even after you get a job.
• I can help you build a long‑term development trajectory, both from a technical and a career perspective.

Most likely, if you’re reading this, you already know what machine learning is, and maybe you’ve even worked in this field. However, it might also be the case that you haven’t yet decided on a field - you’re looking into ML, but you think it’s very difficult and you’ve heard advice to choose testing, development, analytics, or any other field instead.

I’m convinced that ML is currently the coolest and most interesting field, with many job vacancies, good salaries, lots of interesting tasks, and plenty of prospects. And, of course, for many people, this is the top career and social elevator. Many developers and analysts eventually move here and find more interesting tasks and higher salaries here.

As for the complexity, everything is much simpler than it seems. I hope that on this page I’ve already managed to partially dispel the notion that ML is for a select few, but if needed, I can tell you more in detail - just write to me in a private message, and I’ll send you more detailed information. I sincerely love machine learning and everything related to it, and I will be very glad if you love it too)

Classic ML & DL (including pricing, uplift, ranking, and time series), Computer Vision, NLP/LLM/Agents, RecSys, and more specialized areas.

This isn’t just a list of terms. Over the 6 years that I’ve been working in ML, I’ve actually managed to work on all these areas and have collected materials on each of them. You can take a look at my GitHub repositories - I’ve been collecting many projects there for quite some time.

If you haven’t decided on a direction yet, or these names don’t mean anything to you yet, and you’ve just heard about the hype around NLP/LLM from everywherethere’s, no need to worry - I don’t demand an answer from anyone at the initial stage and often suggest that you decide on it later, when you have more information. You will have access to materials on all directions anyway.

In addition to the main areas mentioned above, I have some knowledge and connections in a number of more specialized fields, such as Applied ML Research, HFT, and so on. I can share overview materials, provide useful contacts, and so on in these areas, but I don’t prepare end‑to‑end solutions up to an offer in them due to the low reproducibility of the process.

When selecting students, I naturally pay attention to their background (so I suggest everyone write about it in detail right away), but for me, overall motivation and the ability to learn are more important here than having direct experience in IT and ML - in any case, I help them learn everything from scratch.

Age is also not a problem; I have students aged 16 to 55, and the same goes for education: I’ve helped people get a job after they graduated from school, after college, with irrelevant higher education, or even without it.

For people who are starting from scratch, the roadmap contains all the materials on Python, mathematics, and other basic topics. For people with experience, I will help you find shortcuts that will allow you to benefit from your existing experience and speed up your learning.

Also, all blocks have a basic and an advanced level; going through the first one will provide you with the minimum required to get your first job, while going through the second one will help you delve deeper into the topics - but this can be done later. Whether you’re looking for your first job or a new one, you’ll have lifelong access to all the materials, and you’ll be able to delve deeper into them and apply them even more, including after you get hired.

1. The main goal of my mentoring is not just to teach you the technical aspects of machine learning, but to provide you with all the knowledge that will help you find a job and continue to develop in the field; that is why the main charge for my services comes after you are employed.
2. Graduates of many popular courses, even if they contain high‑quality technical material, often find themselves unable to find a job, and the “guaranteed employment” clause in the contract is often limited to an internship for $500. The reason for this is that these courses do not contain up‑to‑date information about the labour market situation, and in general, they have a payment structure that does not motivate students to find a job faster; sometimes, they even benefit from students studying for as long as possible.
3. Another advantage of my mentoring is the individual approach. From the very beginning, I share tips on how to move more effectively along the roadmap tailored specifically to you, and throughout the entire learning process, you will be guided by me and my team of practicing specialists, not by a tired junior mentor working part‑time for a bowl of ramen.
4. In addition, my understanding of mentoring is broader than just helping with the first job. I strive to ensure that my students grow not only in ML but also in other areas of life, develop in a well‑rounded way, and continue growing after they get a job-— for this purpose, I also create a lot of videos and guides.
5. And finally, I don’t take just anyone on as a student. If I don’t feel a common vibe, I might turn the person down. This way, we don’t just get a general chat - we get a strong and active community of truly motivated people, which accelerates development even more.

1. I DON’T ping you every week, I don’t ask every day “how are you?” and I don’t push you - this isn’t a course with deadlines, everyone moves at their own pace. The responsibility to report both progress and difficulties lies with each student — you can write to me about this at any time.
2. I DON’T sit with you at job interviews and don’t go through them for you. My strategy is to prepare you as thoroughly as possible for them so that you won’t have any problems passing them - both overall and for specific positions. So, even if it were physically possible for me to be at every interview, it probably wouldn’t be necessary.
3. I DON’T teach you secret vibe coding techniques that allow you to complete training in a week and work for 5 minutes a day. Although all necessary material on coding assistants is also provided.

Just like any other mentor, my main goal is to help you get the highest salary in the shortest possible time; after all, that’s what I get paid for.

The only difference is that I don’t earn money by conducting job interviews for you with ChatGPT and then leaving you fend for yourself. My goal is to provide you with real knowledge that will allow you not just to slip into your first job, but to stay and develop in the industry for a long time.

I myself have students who got jobs with high salaries in just a couple of months, starting from scratch, and I’ll be only too happy if you manage to do the same, but in my position, it would be unfair to present such cases as the median.

If you urgently need money and it’s important for you to find a job in a very short time, then nothing prevents you from also aggressively cutting corners in my roadmap, but in my opinion, that’s not the best strategy.

I mentor personally, and so do my partners; I don’t have any tutors.

=What I do=

• I compile and update all materials and the roadmap
• I help with onboarding and provide personal advice regarding the roadmap
• I’m responsible for the strategy, new formats and options, and so on
• I hold meetings and general community calls every week
• I record author‑written guides and create new materials
• I validate the work of my teammates
• I answer all your questions in private messages

=What do teammates do=

• Conduct code reviews
• Conduct mock interviews between modules
• Help with adapting experience and creating a leged
• Help with questions related to the legend during the job search stage
• Validate your interview recordings
• Assign themselves to you during the probation period if necessary

=About teammates=

• All my colleagues are people with extensive experience.
• They all have strong commercial experience and extensive experience conducting interviews.
• They all have strong teaching experience, gained by conducting hundreds of interviews, code reviews, and other tasks.
• Each of them is responsible for a specific area: Classic ML, CV, scoring, RecSys, NLP/LLM.
• Each of them undergoes selection and thorough preparation.
• All my partners work for me on a permanent basis; they’re not freelance students who come for a month and then leave, which is why they have very good experience.
• Therefore, this is NOT the format that has become widespread recently among courses and mentors, where, instead of an experienced person, you essentially have another student who started the training before you conduct the interviews.

=About tutors=

I don’t have tutors in the classical sense. Usually, this refers to students who work for a pittance, regularly tag you in private messages, and write copy‑pasted messages with words of support. Organizing something like this is not difficult or expensive, but I don’t see the need for it. I can afford this thanks to the clear structure of the training, where everyone can quickly and easily find answers to 95 % of their questions. And the remaining ones can always be asked directly to me or my partners, and you’ll get a high‑quality and fast response (we always reply within 24 hours).

My students get jobs at a wide variety of local and international companies, from small to large ones, in a wide range of fields.

We don’t focus on a single niche, like FinTech companies, but try to cover as many areas as possible, since ML is now used literally everywhere, and you never know where that perfect job might turn up.

The main principle

You should not accept an offer that you don’t like. If you suddenly receive an offer that doesn’t meet your expectations in any way - whether it’s about the salary, remote work vs. office, tasks, ethical considerations, or any other factors - you can choose not to accept it, and we’ll continue looking for a job.

It's not a problem.

There are students getting a job who, shortly before starting their training, previously responded to ML job postings with different experience, to job postings in other fields, or even to jobs that are not related to IT.

A few simple tricks can reduce the likelihood of a resume match from 90% to 10%.

No, that’s not necessary.

My mentoring is designed from the outset for long‑term work - you’ll have access to all the materials and guides, the community, and the roadmap, the topics of which go far beyond the basic minimum required for your first job and allow you to grow even further.

Also, if you have any questions after you’re hired, you can always turn to me with them.

If you want to re‑order the validation of a new resume and the drafting / validation of a new legend, as well as full support at all stages of the job search, then now this can be done for 50 % of the new offer. If you suddenly need support at the new job during the probationary period, you can add this for 30 % of the offer, but it’s unlikely that many people will need it, because even in the case of the first job, 85 % of people don’t ask any questions during the probationary period and successfully pass it.

I work fully officially.

Payment is made via card.

A master agreement is in effect for payments; it can be found at the bottom of the website.

Here, I’ll provide a more detailed answer.

This is a fairly common point of view, and it doesn’t come from nowhere. I often see situations where beginners, due to the lack of a clear roadmap and action plan, study ML for years but still don’t feel confident in their knowledge and can’t get a job. And when they finally do get their first internship after 2 years, with a salary of $500, many of them, of course, start claiming that there’s no way to go through this path faster and more effectively.

Some of these people study everything in a random way; some hope for a miracle; some spend years studying mathematics and algorithms (no exaggeration) instead of moving on to practice; some, out of principle, don’t want to ask anyone for advice, let alone pay anyone for help; some are sincerely convinced that the only way to develop is to work for free; and there are many other destructive beliefs and actions, each of which can lead to a great deal of wasted time.

However, the situation changes dramatically when, instead of watching random lectures and solving random problems, the student follows a clear roadmap and has a personal mentor who has already been through this path and knows all the necessary nuances; my statistics confirm this.

At the same time, the vast majority of employers still want to fill their vacancies with people who are already experienced specialists and have an understanding of real‑world work, rather than those who have many educational projects on GitHub; this is also largely what my training is aimed at. Internships still exist, but they’re no longer the format where you’ll be taught everything from scratch; in most cases, the competition for them is higher than for mid-level and senior positions. In such a situation, it’s critically important to know all the nuances, shortcuts, and workarounds that aren’t taught in courses and without which it’s very difficult to qualify for a good grade, regardless of technical knowledge. This is precisely what provides the main competitive advantage over other people.

I often use myself as an example. From the outside, my experience may seem very concentrated to many people, but it still involves a lot of mistakes and wasted time. Now my students get jobs after just a few months of training and reach the same level of income that I was able to achieve only after several years of studying ML, and I’m happy for each of them. I studied everything by myself, without a mentor, and even after winning many competitions and projects, I didn’t know how to get a job or what needed to be done for that. I not only accumulated far more achievements than others, but also made far more mistakes. And my task here is to help you avoid the second one and get as much of the first one as possible.

And of course, there are numerous cases after a person’s employment where his friends, who previously said that it’s impossible, come to me to study)