Data Workshop

Practical Machine Learning
for Programmers*

* For people who know or willing to learn Python (the easiest language to learn for beginners)
Learn by examples, without overwhelmed by mathematics details
Learn how to build end-to-end solution

The World needs a ML specialist
Trend No. 1: AI & Advanced Machine Learning
AI and machine learning (ML), which include technologies such as deep learning, neural networks and natural-language processing, can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously.

Systems can learn and change future behavior, leading to the creation of more intelligent devices and programs. The combination of extensive parallel processing power, advanced algorithms and massive data sets to feed the algorithms has unleashed this new era.

AI is the new electricity!
Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.
Andrew Ng, AI Expert
The current AI way is poised to finally break through.
In 2016, companies invested $26B to $39B in AI.

What Problems Might You Have with ML?

Well... my guess there're some :)
The Beginning Is Always The Hardest
You feel lost, and you do not know how to start? The amount of information is too much. What learn first? And finally, do you feel confused. Generally, you want to get knowledge that will be useful for you.
Lack of Hands-on Experience
Might you read books or other materials, but still this is not enough to be a machine machine learning specialist. You need more practical things and less theory. For simple reason - you want to solve real problems with code.
The way how machine learning usually explains looks so hard, especially for programmers. There is a lot of complicated mathematical description of algorithms. Sometimes you feel that you need to do a PhD in mathematics to get it.
Lack of Time
You really want to shift your career into ML, but you have a family and it's very difficult to find a time to do this. I also have family and for me is clear how hard is to find a balance.
Lack of Environment
You want to try some machine learning models, but the configuration of the environment is so hard (especially for Windows). Also, you need more powerful hardware than the own laptop.
Lack of Intuition
No intuitive understanding of tasks, models, metrics, parameters. You want to know more about algorithms to choose the one that's right for given problem. Understand why this, and not another one.
Lack of Knowledge about Right Tools
There're a tons of tools and platforms. Which one you should select for given problem? To find an answer why this or another one. It can be hard...
Forgot Mathematical Notation
Might you have calculus and linear algebra at university or school. But it was some time ago. Your mind works differently now. You prefer to see a piece of code, rather than intricate mathematical formulas.
Lack of Data
You want start play around with real problems, but you don't know how to find some interesting dataset (not only Iris or MNIST). To find a data is really hard, because usually companies will not share them.
Last Mile
Might you know how to build a model, but you're missing knowledge how to deploy a model to server, update a model or exposing a model via API.
Lack of Like-Minded People
You do not know the people in your environment who are actually interested in machine learning things and work on their skills.
Too Theoretical
ML/AI relatively old topics... theory was started since 50s. There's nothing wrong with theoretics. But theoretics has its limitations with adoption to real life.
Data Scientist: The Sexiest Job of the 21st Century
Harvard Business Review
Vladimir Alekseichenko
Founder & Trainer
Vladimir found the Data Workshop.

Dreamer & Father.
Start program in 2006.
Start use machine learning from 2013.
Machine learning trainer (organized 9 workshops).
Perfectionist in the heart and pragmatic in the mind.
Host podcast about Artificial Intelligence (in Polish).
Architect at General Electric.
Public speaker (50+ talks).
Traveler (visited 27 countries).

He loves (data) challenges.
DataWorkshop in Numbers
It is the best way to improve your professional skills and become more valued.
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What people said about DataWorkshop?
Łukasz Murawski
Network Quality Sr Specialist at Polkomtel

I highly recommend DataWorkshop. It's been a great value to me. Vladimir has definitely the knowledge and experience in the field.

However, what makes this course special is in my opinion his approach to teaching:
Students first - speaking the language adjusted to the audience
Top-down approach - high level concept first, details afterwards
Clarity - Vladimir's ability to explain difficult topics in a simple words
Real life examples and analogies

Although I'm a telecom engineer and not a certified programmer, after DataWorkshop I've managed to successfully implement a few ML models at work.

I recommend this course for anyone, who wants to get to grips with ML and AI quickly.
CTO at

DataWorkshop helped me to systematize my knowledge and gave possibility to use well-prepared example tasks on practice.

Thanks to the workshop I met other people interested in the topic, I could exchange knowledge and learned more about XGBoost.

It is worth to take part in workshops, being run in a good atmosphere, and Vladimir has the gift of translating complicated things in easy ways.

If you would like to try yourself in machine learning or talk to others about your experience, I would recommend you Vladimir's workshops.
Data Engineer at GE

I went to DataWorkshop to study, and to review what I knew about Machine Learning so far.

Mostly, I was interested in getting to know Kaggle and gaining practical skills, such as using Jupyter Notebooks and writing algorithms there. It is worth to emphasize that Vladimir runs the workshop in such way that it is possible easily follow him without special preparation.

Thanks to clear instructions for workshop preparation, I built the Anaconda environment and installed basic Python packages for machine learning.

During the workshops I learned how Python libraries work in Data Science and what they serve. Till that period I lost a lot of time to build the environment, choose Python version and libraries that are supported per version. Workshops (and preparation for workshops) helped me to deal with it in a very short time.

Workshops take place in nice places and have a pleasant atmosphere and teamwork. Vladimir has very good approach, he starts with little difficulty and gradually diversifies the examples and develops the main problem, encourages questions and discussions.

Workshops convinced me that it is worth to explore Data Science, get interested in Kaggle and what is important, this is not unreachable if you devote time to it. They motivated me to continue to develop, and thus to change jobs. Nowadays I work as a Data Engineer at General Electric.

I would recommend workshops to anyone who is interested in Data Science and wants to learn how to define a problem and sort it out. If you like asking questions and looking for answers, I would highly recommend it.
Customer Success Engineer at

I recommend DataWorkshop for all the people who are curious about the world. The first time I came to the DataWorkshop I had no idea about Artificial Intelligence not to mention about its implementation in Python.

Today, I am able to solve similar problems on a production data by myself. I did not only learn how to implement various algorithms, but also I've learned many interesting facts about the world, because each time we worked with the real data.

I do not hide the fact that sometimes Vladimir introduced elements of competition between us, what further was motivating to achieve better result than our neighbors. I will also add that it is a very good possibility for networking, because you can meet interesting people from different industries, all curious about the data.
Software Developer at ATSI

DataWorkshop with Vladimir is a series of successful meetups on data science domain.

Targeted for beginners and intermediate level scientists, who want to expand their horizons with brilliant cases shared by Vladimir.

Very friendly and informal atmosphere encourages audience to actively participate in workshops and even takeover leading the meeting with their stories (for a few minutes of course).

His broad and deep knowledge on AI and Machine Learning let him inspire the participants and teach them new tools and techniques.

It is hard to organize workshops well, especially for people from different technological backgrounds. But DataWorkshop was a smooth ride, you just needed to follow the instructions provided in advance.

Great thanks :D
Software Consultant at Sabre

Vladmir, in a very friendly, reliable and practical way explains complex Machine Learning (ML) models and algorithms. Does an overview of current ML algorithms and practical methods of using them.

Why attend DataWorkshop? High level of classes and interesting ways to present topics. Currently I do not use AI but I have plans for the nearest future.

I recommend DataWorkshop to people who would like to start their adventure with Artificial Intelligence and practical ways of using them.
Data Scientist

Being fascinated with machine learning I was looking for different ways to increase my knowledge and skills, and luckily I found Vladimir's workshops.

Thanks to them, I got to know the method of Gradient Boosting, which give you great results in practice, but is not very famous. Also, I practiced and developed other methods such as neural networks or hyperparameters tuning.

Workshops are good opportunity to meet people who really learn ML, very popular subject recently, many people talk about it, but only few do. For people who want to explore ML I recommend workshops as an important element of learning process.
Data Scientist at edrone

I participated in all workshops. It was (is) an extraordinary journey through machine learning. Vladimir, in a simple and interesting way, presented complex topics: features engineering, visualization, cross-validation, gradient boosting or regularization.

Big plus of the meetings is source code in python done during classes.
Workshops are well prepared, and apart from the presentation, they bring you closer to use of interesting libraries (eg xgboost, keras).

Besides knowledge of ML, I also got a practical knowledge of tools (python, jupyter notebook), which is useful for me.

Meetups with Vladimir are recommended to all people who are interested in ML and BigData.
Full Stack Developer at Guidewire

As someone with background in IT but totally new to Machine Learning. I really enjoyed participating in DataWorkshop with Vladimir. Training was well prepared and organized in multiple small steps.

That helped everyone to keep on track - on each step I've had the time to understand what and why we are doing. If some concept was easy for me to understand I could spend that time exploring more complex solutions trying to improve provided code.

I highly recommend Data Workshop to everyone who has small or no experience with machine learning. Please have in mind that prior knowledge of any programming language and command line commands will be quite handy.
Machine Learning Engineer at

At the workshops I learned how to carry out the whole process of machine learning from data preparation through model training to evaluation and use.

Vladimir easily explained the issues related to this, often referring to the real examples of the business value of machine learning.

DataWorkshop improved my knowledge primarily in the practical use of popular libraries for solving real problems.

Atack Plan

It is a simple and pragmatic way to build your practical skills in machine learning
8 modules
Logically related modules. One module per week.
64+ hours
You will spend to gain experience. With a big emphasis on practice.
+8 extra hours
We will sped together on webinars to analyze homework and other topics.
56 days
So many days will take the workshop where you will learn about machine learning.


Machine Learning Overview
You'll learn
✓ Basic concepts, such as features, target variable, objects.
✓ Basic machine learning workflow (including feature engineering, feature selection, model selection, model evaluation).
✓ Type of ML tasks: regression and classification.

You'll build two real (machine learning) models.

1. regression task, you will predict the weight based on height and gender.
2. classification task, you will predict a gender based on name.

You'll learn
What is...
✓ ... Gradient Boosting.
✓ ... some related concepts (e.g. decision trees)
✓ Experiments with several (the best ones) implementations: CatBoost, XGBoost, LightGBM.

You'll build ...

You'll build ...

Gradient Boosting
You'll learn
✓ what is Gradient Boosting
✓ some related concepts (e.g. decision trees)
✓ experiments with several (the best ones) implementations: CatBoost, XGBoost, LightGBM


You'll build ...
1. ... a model to predict credit score risk

You'll build ...
Natural Language Processing
You'll learn
What is...
✓ ...
✓ ...
✓ ..


You'll build ...

You'll build ...
Multilayer Perceptron
You'll learn
What is:
✓ ... the neural network (deep learning) model?
✓ ... layers (input, hidden, output) and deep learning?
✓ ... backpropagation, dropout and batch normalization?

You'll build ...
1. ... pure implementation a neural network (without any framework) to understand better how it works inside.
2. ... your own neural network and keras implementation to recognize handwritten digits in MNIST dataset.

When you've been devastated by a serious car accident, your focus is on the things that matter the most: family, friends, and other loved ones. Pushing paper with your insurance agent is the last place you want your time or mental energy spent.

Apply a neural network to predict claims severity for insuriance company.

Convolutional Neural Networks
You'll learn
What is:
✓ ... map features (kernels), max/average pooling.
✓ ... stride, padding.
✓ ... data augmentation: flipping, rotation, projection.

You'll build a model to recognize ...
1. ... 10 class from fashion world by using Fashion MNIST.
2. ... German Traffic Signs and try to outperform an average human (~98.8%).

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224×224) input image.
This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.

What about combining two ideas: Spatial Pyramid (pooling) and Convolutional Neural Networks?
Transfer Learning
You'll learn
What is ...
✓ ... transfer learning
✓ ... pretrained models and explore available: LeNet, VGG, ResNet, Inception
✓ ... fine-tuning (also when and how)

You'll build ...
1. ... amazonka

Every minute, the world loses an area of forest the size of 48 football fields. Better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively.

You are challenging to label satellite image chips with atmospheric conditions and various classes of land cover/land use. More details - Planet: Understanding the Amazon from Space.

Recurrent Neural Networks
You'll learn
What is:
✓ ... recurrent neural network
✓ ... long-short term memory (LSTM)
✓ ... gated recurrent unit (GRU)

You'll build ...

1. your own chatbot based on Cornell Movie Dialogs Corpus dataset
The first is cheaper at least by 50%!
Early Registration Deadline is October 1!
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Satisfaction Guaranteed
Or your money back (up to 14 days)!
I really care to help you. But, I have enough humility to accept that I'm a human as well. And I can help some people. Who actually accept my approach to learning.

If you realized that workshop is something else what you expected, just drop me a line and I guarantee that I will return the money.
Wybierz swój pakiet
Tylko jeden raz cena kursu obniżona o 50%!
1 osoba
1080 zł 540 zł
  • 8 godzin lekcji do praktycznej wiedzy
  • 8 zadań domowych - projektów do realizacji
  • Software: skonfigurowane środowisko jupyter (które można re-użyć na własny użytek później)
  • Dostęp do zamkniętej grupy na Facebook
  • Sprawdzanie zadań domowych
  • Linki pomocne
1 osoba
4700 zł 2350 zł
  • Pakiet Gold
  • Trening indywidualny przez 2 miesiąca - 8 godzin konsultacji (jedna godzina tygodniowo)
  • Wsparcie indywidualne i pomóc w rozwiązaniu blokad
  • Pomóc w rozwoju ścieżki karierowej (przygotowanie się do rozmowy rekrutacyjnej)
  • Możliwość wystawienia (uczciwej) opinii dla przyszłych pracodawców (ale trzeba się postarać, żeby była pozytywna, tylko zapłacić to za mało :)
Contact me
If you have any questions or queries then please feel free to drop me a line.
or you can use

Kraków, Poland
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