ML Foundations Course
for Product Managers

There is a lot of hype around ML. We will overcome it and immediately build a real model that is useful for business, and at the same time, define the basic concepts of ML problems.

You will learn:
→ That it is possible to build an ML model in Excel
→ Main terminology of ML: feature, target, training set, model

What it looks like:
→ You help the CEO solve the problem of demand for rental bicycles
→ Using different industry cases, you practice to determine what features, objective function, and training set are.

Although PMs do not need to understand the internals of algorithms, they need to choose from a variety of problems those that are more efficiently solved using ML, and the ones that can be solved the old-fashioned way or bypassed altogether.

You will learn:
→ Main types of ML problems (regression, classification, ranging, etc) based on the examples from the domain
→ What tasks should be avoided
→ What problems do not require ML at all

What it looks like:
→ You help your colleagues (CFO, Marketer, Jr.PM, CEO) to determine their problem type

When the PM sees that the problem belongs to one of the known categories, it is extremely important to understand what critical data we will need to build a fast MVP, and where to obtain this data.

You will learn:
→ How to identify features for training the first version of the model
→ Five sources which you can get training data from

What it looks like:
→ You help your colleagues to identify the main features for their cases
→ You come up with ideas where to get data for these features

When the PM sees an ML task that is useful for business, selects the first features, and even gets hold of training data datasets, it's time to transfer the scope of work to Data Scientists. Then the magic happens, and the PM returns to the discussion when it's time to talk about model quality metrics (see next lesson). But what kind of a PM doesn’t want to also understand a little about magic?

You will learn:
→ What steps a DS takes when building a model
→ Where to get test and training data sets
→ How the model becomes more complex when it is necessary to take into account more complex dependencies

What it looks like:
→ Together with the Data Scientist, you look at the data and Python code of the bicycle demand model
→ You complicate the model so it becomes a nonlinear one
→ And complicate it even more, so you have two variables now

As with product metrics, the PM does not need to be a master of detail, but it is important to understand the output of the trained model and be able to discuss it with the analytics team.

You will learn:
→ That the Data Scientist will usually give you not one, but several versions of the model
→ What is Recall, Precision, and F1 score for the classification ML model, and why we need them
→ What is RMSE and MAR for the regression model and why we need them

What it looks like:
→ You learn to calculate Recall, Precision, and F1 score based on cases presented
→ Them, you choose which version would be better based on your understanding of this particular business case

It's time to apply all the knowledge gained to a new task from our CEO.

You will learn:
→ How to apply the acquired knowledge from start to finish

What it looks like:
→ You solve the problem of predicting user LTV

We will discuss how ML skills relate to other product skills: analytics, technology and A/B testing

You will learn:
→ How ML relates to API/services, A/B experimenting, and analytics
→ How to grow further in the domain: the PM AI role

What it looks like:
→ You receive a summary checklist and links for further study
→ You receive a personal certificate

Highlight business impact: Explain how AI-prototyping knowledge will help you build smarter products that solve real customer problems and drive growth.
Emphasize upskilling ROI: Show that investing in your skills now means faster delivery, better decisions, and fewer costly mistakes down the road.
Connect to company goals: Align the course benefits with your company’s strategy, whether it’s innovation, efficiency, or entering new markets.
Stress team advantage: Point out how your new AI-prototyping skills will uplift your whole team’s expertise and collaboration.
Assure minimal downtime: Reassure your employer the course is hands-on, flexible, and won’t disrupt your current workload.
Simplify payment: Let them know we can issue an invoice if needed, making the process easy for company reimbursement.

Contact Sales

The cases are really real and reflect the diversity of products in the industry: not everyone has a "website" or a "store". In the "Fundamentals" course, we cover the clothes rental start-up; in the "Tech for PM" simulator, we will look at a taxi ordering app (like Uber), a music service (like Spotify), and a food delivery app. In the simulator "A/B testing" we will dive into experiments on the Meditation service, and in "Product Discovery" we will analyze the business of renting clothes.

The more cases you solve (and not just read/watch), the more experienced a product manager you become. There is no other way.

One of the distinctive features of ProductDo is that all our courses are packaged in a simulator format. We can say that this is an important element of our product vision. The simulator as a learning tool has many wonderful properties:
- it requires constant active participation of the student. In order to move forward, you need to choose some action or answer a question every time.
- it allows you to give detailed feedback on any action: You can justify the preferred option with links and examples
- it allows you to move at your own pace and does not burden those who do not really like to work in a group with unnecessary communication.

Usually, they teach like this: theory → practice. If you have mastered the theory well, then you do the practice 100% or so. We have a completely different methodology. We start with a practical task that needs to be solved (and if the course suits you, then you don't know how to do it yet). Only when you have tried to solve the problem, we bring up some theory behind it. We believe that the cycle: trying to think independently → making a decision → feedback on a decision is a very effective way to master new material, especially for those who have already reached some professional level.

If you want to try the simulator format, take a free lesson. It contains questions from different areas of product management. At the same time, you can evaluate which skills you already have enough pumped, and which can be further enhanced.

We help by giving you a bunch of practical tasks and sharing our mistakes and experiences. We have already built so many products that with our examples we can help you formulate your area (more technical or more payment, related to ML / AI, or sales, etc.). We believe that a good PM chooses their favorite product themselves and does not need to be led by the hand.

The entire history of the simulator with comments and answers is saved. Access to the materials remains forever.

Yes, students who have successfully passed the course may generate a certificate and proudly add it to their LinkedIn profile to Licenses & Certifications, or upload it to Facebook in the Schools section.

Yet do keep in mind that upon applying to a product company, certificates do not really increase the chances of being hired, at least in our practice. If a resume mentions a certain course, then this may raise questions about what the candidate really knows right now. Therefore, you need to solve problems and develop skills, and not chase certificates. Fortunately, our courses have enough practice.

You can be sure of the materials' quality and our mentors' experience. We've been teaching for many years, and we have thousands of happy PMs. Feel free to take a look at the student reviews for verification.

If you passed less than 30% of tasks of the simulator you could refund your money in full.