Practical machine learning*


*
For those who know or want to know Python, one of the easiest languages for beginners.
Learning through examples, without sophisticated mathematical details
Learn how to implement throughout solutions.


The course starts soon.
Join the pre-sale list and I'll give you discount :)
Artificial Intelligence is gaining momentum.
Companies invested from $26bln to $39bln
in artificial intelligence already in 2016.

McKinsey
About the course
Easy and pragmatic way of getting practical machine learning skills.
Let's go!
Theory
8 hours of theory built on real examples. It will boost your machine learning knowledge and is prepared for you in 3 convenient formats to choose from: audio, video and text.
Practice
Over 30 cases to solve. Available on all devices. You just need WiFi.
Support
Thanks to dedicated group on Slack, you'll be able to get support, expand networking and stay motivated to develop.
Environment
You don't have to install anything specifically. I've prepared ready to go Jupyter environment specially for you.
Webinars
I'll answer all your questions after each topic, I'll evaluate your code line by line together with discussing homeworks (Gold and Premium packages).
Feedback
Weekly feedback sessions will ensure solid support for you and will help to execute own project (Premium).
Numbers
modules
One module a week equals 8h of theory.
or more hours
you will spend on gaining experience, focusing on practice
extra hours
That much time we will spend on webinars, discussing homeworks and other matters.
days
That's the total course duration, we'll finish before Christmas.

What Problems are you going to solve?

1
Beginnings are always tough
Do you feel lost and you don't know how to start using machine learning in practice? There is information overload and you are stuck? I'll help you finding the right knowledge which you will comprehend in a short time.
2
Lack of practice
You've been reading a lot of books and resources but this is still insufficient to become a machine learning specialist.

You need more tangible knowledge based on real examples. I'll get you there.
3
Too complicated
Machine learning is usually explained using academic language which tends to be incomprehensible, especially for coders. Sometimes you even feel that you need a mathematics PhD to grasp some algorithms. I'll show you that this is not necessary.
4
Lack of time
You're dreaming about becoming a machine learning specialist but you've got a family and plenty of duties. You're short on time to research valuable knowledge in the information overload. I'll help you to find the right balance.
5
Lack of environment
You want to try an algorithm or a library but the environment setup is too complicated, especially on Windows. Furthermore, you have an old laptop. No worries, I've prepared a ready to go environment for you.
6
Lack of intuition
You don't understand tasks, models, metrics and parameters on the intuitive level. You want to easily recognize which algorithm fits best to a particular problem. I'll explain that to you simply.
7
Lack of knowledge about the tools
There are tens or even hundreds of various tools and platforms. You don't know which one to choose for a particular problem ad understand why this one is superior over the others. I'll share my experience with you.
8
You don't remember maths
You probably recall calculus, you know a bit about integrals, but almost everything left your mind right after studies. You prefer to see a bit of code rather than mathematical equation? I've got the same :)
9
Lack of data
You want to tackle real problems but you only have access to sandbox data (like Iris). Finding the right data is a challenge, but I have several hints for you.
10
The last step
You know how to build a prototype, but this is only partial success. The real value comes up only when your models is deployed on production. I'll show you several options.
11
The people
Right environment develops you. Are you seeking people who share your interests? Relax, you'll meet them on the course :)
12
Too theoretical
Machine learning, as well as artificial intelligences emerged in 50-ties last century. There is nothing wrong with theory, but theory is coined through experiments. You'll have lots of practice.
Do you want to master machine learning?
You just have to go through several easy steps.
Who?
Vladimir Alekseichenko
Tutor/coach and Creator
I founded DataWorkshop in 2015

I started coding in 2006 and using machine learning in 2013.

I'm machine learning tutor and I have carried out around 25 workshops for around 1000 participants.

I run a podcast (PL) about AI.

I've given over 70 presentations.

I'm a perfectionist in my heart but pragmatic in my mind.

I'm father, dreamer and traveller - I've visited 28 countries so far.

I love helping others, analyzing data and facing all sorts of challenges.
Technologies
Python
Jupyter
Keras
NumPy
Pandas
Scikit-learn
Matplotlib
XGBoost
TensorFlow
Google Cloud Platform
CatBoost
MxNet

Agenda

1
Machine Learning Fundamentals
You will learn:

✓ features, target variable,objects
✓ feature engineering, feature selection, model selection
✓ wo primary tasks: regression and classification

+ Bonus
2
Dive into machine learning
You will learn:

✓ how to navigate through sklearn library and handle different such as decision trees, random forest and other
✓ why proper model validation is so essential, such as cross-validation or other
✓ why visualization is your friend and what are the simple but effective tips to do it right

+ Bonus
3
Gradient Boosting
You will learn:

✓ What is Gradient Boosting
✓ Verify it on several best implementations of gradient boosting: CatBoost, XGBoost, LightGBM

✓ Pragmatic way of hyperparameter tuning

+ Bonus
4
Feature Engineering
You will learn:

feature engineering for:
✓ ... continuous values
✓ ... categorical values
✓ ... dates and other

+ Bonus
5
Multilayer Perceptron
You will learn:

✓ What are neural networks (deep learning)?
✓ What are layers (input, hidden, output) and when layers start to be deep ;)?
✓ ... backpropagation, dropout i batch normalization?

+ Bonus
6
Convolutional networks
You will learn about:

✓ map features (kernels), max/average pooling.
✓ stride, padding.
✓ data augmentation: flipping, rotation, projection.

+ Bonus
7
Transfer Learning
You will learn about:

✓ transfer learning
✓ pretrained models and explore available: LeNet, VGG, ResNet, Inception
✓ fine-tuning (when and how)

+ Bonus

8
Production and good practices
You will learn:

✓ To what you should pay extra attention when deploying a model on production
✓ What does result repetition mean and why is it so important
✓ How to version a model and switch it on production (without user access)


+ Bonus
More practice?
There will be a contest as a part of the course
where you will use gained knowledge to solve a real problem.

… and yes, you could win prize :)
After completing the course
you will receive* a CERTIFICATE
* for Gold and Premium packages
Co ludzie mówią o DataWorkshop?
Software Engineering Leader w Nokia

The knowledge stays in your heads after the Practical machine learning course.

Attendees complete the course equipped with knowledge, materials and skills which allow them to start own projects.

The course includes many practical examples, ready to use scripts, theoretical explanations and links for further reading. I recommend this course to people who want to begin their adventure with machine learning and they're looking for a place to get practical skills.
Software Development Manager w IBM

Well prepared and valuable course - in my opinion this is super training to start with machine learning. I do recommend.
Senior Software Developer w Levi Strauss & Co.

The course organized by dataworkshop.eu is the best way to discover ML in practice and start building models on your own. If you are interested in ML, this course is the best way to start.
CTO w FocusNet

Remarkably vast and substantial course based on practical assignments.

Perfectly prepared environment and materials allow focusing on exploring the content. Furthermore, attendees create a very helpful community.

I recommend it to everyone, not only to coders.
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.
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
Data Scientist: The Sexiest Job of the 21st Century
Harvard Business Review
Satisfaction guaranteed
or money return (up to 14 days since the course launch)!
I really do care about helping you, but I'm also humble and aware that I'm just a human :) I realize that I can help only people who find my approach appealing.

If it occurs that my approach doesn't resonate with you I guarantee that I will give you your money back. You have 14 days from the course launch to figure out if you are into it or not. I'll be grateful for the feedback.

Choose your package
Basic
1 person
750 EUR
+ VAT (23%)
  • 8 hours of video resources (+ separate audio track as a podcast)
  • 32 jupyter notebooks with materials (including homeworks)
  • Software: configured Jupyter environment (which you can re-use later for own purposes)
  • Access to closed Slack group
  • A 100 page book
  • Extra links
Premium
1 person
1650 EUR
+ VAT (23%)
  • Gold package
  • +
  • Individual training for 2 months - 8 hours of sessions (once a week)
  • Individual support and assistance in removing limits
  • Assistance in developing career path (preparing to job interview)
  • Option to receive an honest assessment for future employers (but you need to work hard to earn a positive opinion, just paying is not enough :)
FAQ
Will the course be online?
Yes, the course is 100% online.
How much time is for personal work in each module (week)?
The recommended time is 8 or more hours a week. Also for Gold and Premium packages there will be an additional hour for Saturday's webinars and for the Premium package there will be an additional hour for individual consultations. I highly recommend doing it regularly during the week, e.g. one and a half or two hours in the morning, when the mind is rested. Then it will be easier to digest the information.
How will consultations in the Premium Package look like?
These are Skype meetings (at least 1 hour a week). The individual consultation will be available at that time. We will talk about assignments. It's best to deliver a list of specific questions before the meeting, so I can prepare answers accordingly to make the best possible use of our time. Also during meetings I will advise on topics related to recruitment, among others how best to prepare for it, what to pay special attention to, etc.
Is it going to be a video with a lesson or something else? An hour or more? And how should I do my assignments? Who and how will check them?
The course consists of 8 modules. One module per week, therefore the whole course will take 8 weeks. One module will contain at least one hour of video with an explanation. Also, each module will have homework assignments. Assignments will be done in an environment that will be set up by me. We will discuss common mistakes or difficulties during webinars (available for Gold and Premium packages) or on individual consultations (Premium package).
What will the scope of tasks? Will there be classification problems (products, clients), NLP, image recognition?
There will be both classification and regression. It will also concern neural networks (in particular convolutional and ??recurrent networks). At the end, there will be a module on good machine learning practices and implementation of models on production. Read more in the "Agenda" block.
On what statistics / math level can you start thinking about data science? Is it knowledge that can be obtained independently, without being a graduate of any of these academic courses?
I don't require graduating mathematics or statistics, on the contrary, I'm afraid that people following these directions may be surprised :). I explain it completely differently than it is done on above mentioned academic courses. I avoid equations or other intricate definitions, I focus a lot on analogy and examples.
I would like to master machine learning, and eventually learn about deep learning and the openAI package to simple bot for gaming. When exactly will the course take place?
The course is 100% online and there is one module per week. Each participant decides when to do it. Personally, I highly recommend doing it in the morning, just after getting up earlier, when the mind is still able to embrace new knowledge. In the evening after work, it can be harder, because then the brain is tired. Everybody has to make the choice themselves :). There will be quite a lot on deep learning in the course (I recommend checking the agenda for modules 5-7). Writing a bot for games works well in the area of reinforcement learning, a very interesting topic, but it won't be in this course due to its complexity. It is planned for the next course if there is a demand.
I'm interested in time series forecasting using deep learning.
That sounds appealing, as I usually get these questions from people who want to be more savvy at cryptocurrency prediction. In this course we will dive into recurrent networks which work quite fine with times series. Speaking of time series, it's better to simplify at the beginning. Instead of predicting what will be the specific product price in a month we could predict if that price will be higher than X. It turns the problem into a binary classification which will be widely covered. Such model will be more precise as the task is simpler. That is sufficient for the business to make the right decisions. (for instance if the BTC price will be higher than 7000$). I'm planning another course on time series forecasting. I've got experience on currency markets. I'll work on the course if there is demand for that.
I'm a person with social sciences background (psychology), I've been programming in Python for 1.5 years. I managed to build several classificators (mostly svm/rf in scikit-learn) - is this course right for me?
Absolutely! More practice first, as I see SVM or RF mentioned in the question, they sometimes work quite ok too. Usually xgboosting outperforms them, being more stable and faster at the same time. Neural networks will be another added value. We will learn how to process images. Finally I will speak about good practices when in comes to deploying models on production as it is critical but often neglected area. We will work on numerous cases and examples that are good source of practicing and understanding all essential aspects.
How vast theoretical knowledge should I gain to navigate efficiently in machine learning world?
That's tough question. Let me ask you this way - how vast knowledge is necessary to drive a car? Engineering features and mechanics, these are complicated issues, but how many drivers actually know a lot about them? The point of this course is to gain skills and proper recognition rather than understanding all theory.
Isn't the course too difficult for an analyst, who isn't coding on daily basis? I know Python a bit, but not fluently, I'm hesitating if this course is a good fit for me.
I believe it will be fine. Programming is just a tool. Developing right mindset is more challenging. This is a combination of logic and strategic thinking. If you feel that you've got these qualities the difficulty level will be acceptable. Read some course reviews and enroll anyway. You've got to weeks to resign if it will be overwhelming.
What will be the learning methodology?
I'm self taught. I have filtered all that knowledge and I teach through examples and analogies. I believe that this is the biggest asset of this course. The first step is to explain what problem we want to solve using examples and analogies, then we move on to technical level where we focus on code rather than just on mathematical equations.
Can I receive the invoice?
Yes, there is an option to issue an invoice upon ordering. Just select "I want invoice".
Do you want to learn more about me?
The beginnings are always tough, the goal is vital.
I've been fascinated in computers since I was a kid. First, I was staying at school after lessons, that was the only way to spend some time with a computer those days.

When I was 15 I went to a big city to earn for my first computer.
When I was 18 I went abroad in search of a place where I could study.
At 19 I moved to Poland and I started IT studies.

At that time I was the happiest man on Earth, I had everything:
  • funds for living (scholarships)
  • a place to study (room in a dorm)
  • access to knowledge (internet and libraries)
I'm self taught. I've come to many things on my own and you can see that.
In 2013 I started dwelling on machine learning. It was a harsh reality check for me. Although the matter was highly appealing for me, the explanatory method were way too embroiled. In most cases it was 'raw' maths. I had been struggling, I was giving up and struggling again, and finally I started to grasp what's it all about :)

I'm self taught. I've come to many things on my own and you can see that. For instance, I skipped learning many definitions and proofs by heart, but I spend more time to understand them on an intuitive level, to check what's this all about and what works better in practice.
It can be simpler though…
At some point I've realized that it can be much easier than you expect at the beginning. It's all because I love sharing knowledge and I started doing that on meetups, workshops or conferences.

So far I have managed to carry out 20 workshops where over 700 participants showed up.

I've drawn many conclusions and I've understood other issues, which emerge in the learning process, more thoroughly. I've done my best to eliminate learning constraints in this course.
Speaking about difficult things simply is a tough challenge!
… your motivation
is the key.
My personal success will be your ability to use the knowledge in practice after the course. I truly mean it. What does it mean in practice? For instance, if you retrain yourself at current workplace or if you'll kickstart your own project that would aim at something practical.

I'll tell you right away that your motivation is essential. Therefore, I'm inviting you enthusiastically only if you really urge to learn machine learning and related topics in order to start using that in your life!
How can I help you?
If you have any questions, please contact me :)
you may also write to:
hello@dataworkshop.eu

Grodzka 42/1, 31-044
Kraków, Polska
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