MACHINE LEARNING FOR BEGINNERS. LEARNING FROM US == CAREERS IN IT
We will help you learn one of the most in-demand IT professions from scratch
TRAINING FORMAT
ONLINE
WHO'S FITTING
from scratch
WHAT ML-ENGINEERS DO:
/ANALYZE
/EVALUATING
large amounts of data and look for patterns in it.
/DEVELOPING
impact of new algorithms on the product and conduct experiments.
applications and infrastructure to automate the operation of ML solutions.
/CONNECTING
machine learning models and neural networks that help businesses make decisions.
WHO THIS COURSE IS FOR:
ANALYTICS
MATHEMATICIANS
Already working with data, but want to learn advanced techniques and tools to take your analytics to the next level.
Passionate about maths and science, but want to move from theory to practice and learn how to solve real business problems.
BEGINNERS
DEVELOPERS
Want to master machine learning but don't know where to start. This course will give you all the knowledge you need to start a career in ML.
Have experience in programming and application development, but want to apply your knowledge in a new area and learn how to identify business needs.
YOUR CV == IN 7 MONTHS
Linda L. Gardner
Junior ML-engineer
I know how to develop applications in Python, know the basics of object-oriented programming. I know libraries for data analysis and machine learning: pandas, numpy, matplotlib, seaborn, scipy, sklearn
I know SQL syntax, can write database queries and work
with SQLAlchemy.
I know the basics of backend development and FastAPI framework.
I use Git to version applications and work on projects.
I use Airflow to automate regular tasks
I know classical ML algorithms and know how to build ML models for solving regression, classification and clustering problems.
I have experience in applying advanced ML-models based on gradient bousting: CatBoost, LightGBM, XGBoost.
Familiar with neural networks and PyTorch* library. I am able to solve tasks on working with texts and images: classification, object detection, face recognition, text generation.
I understand the basics of probability theory and mathematical statistics. I know basic statistical criteria and conditions of their applicability.
I know how to conduct A/B-tests and evaluate the influence of ML-models on the product. I understand how to calculate the sample size, effect size and duration of the experiment.
I know SQL syntax, can write database queries and work
with SQLAlchemy.
I know the basics of backend development and FastAPI framework.
I use Git to version applications and work on projects.
I use Airflow to automate regular tasks
I know classical ML algorithms and know how to build ML models for solving regression, classification and clustering problems.
I have experience in applying advanced ML-models based on gradient bousting: CatBoost, LightGBM, XGBoost.
Familiar with neural networks and PyTorch* library. I am able to solve tasks on working with texts and images: classification, object detection, face recognition, text generation.
I understand the basics of probability theory and mathematical statistics. I know basic statistical criteria and conditions of their applicability.
I know how to conduct A/B-tests and evaluate the influence of ML-models on the product. I understand how to calculate the sample size, effect size and duration of the experiment.
DESIRED SALARY FROM
$150,000
HOW TRAINING TAKES PLACE
FIND OUT MORE ABOUT THE COURSE
The tutors will talk about the course, discussing each module, its value and the specifics of using the knowledge gained in future work.
TRAINING FORMAT
- Training takes place in an intensive format of 3 lessons per week
- Homework assignments are done on real infrastructure
- All lectures and supplementary materials are available on the education platform and remain with you after the course is over.
- Our students spend an average of 20 hours per week on their studies
- Homework assignments are done on real infrastructure
- All lectures and supplementary materials are available on the education platform and remain with you after the course is over.
- Our students spend an average of 20 hours per week on their studies
WORK ON A REAL PROJECT
You will build a system for ranking posts on a social network. You will work on the project throughout the course, completing assignments as you learn the necessary theory. You'll master the most important areas of machine learning (from tabular data to pictures) and learn about industrial development. In the end, you'll create an API that selects the most relevant posts for each client.
UTILISE OUR INFRASTRUCTURE
- Work in all the tools you need on our infrastructure. You don't have to install software on your computer, we provide access to all technologies
- Practise on data from real-world problems
- Learn solutions from instructors and other students
- Practise on data from real-world problems
- Learn solutions from instructors and other students
ASK ANY SUPPORT QUESTIONS
- Discuss tasks and projects with market experts
- Your mentors will be ML-engineers from leading Russian companies
- Your mentors will be ML-engineers from leading Russian companies
GET A JOB AFTER THE COURSE
At the end of the course, we help our students to find a job: we explain how to write a CV and cover letter, practise self-presentation skills, and then send your CV with recommendations to partner companies. Throughout the entire employment process, we support and accompany our students until they receive the desired offer.
COURSE PROGRAMME ://
We will start with the basics of programming, learn how to write code in Python and learn libraries for data analysis and machine learning. We will learn how to work with databases and how to use SQL queries to get data for models. We will talk about application architecture and learn how to control versions using Git. We will write a prototype of a future ML service and configure everything necessary for its operation.
Let's get acquainted with classical machine learning algorithms. We will consider everything from simple linear models to gradient bousting on decision trees. We will learn how to prepare data for models, tune various parameters and evaluate the quality of ML algorithms. We will discuss the subtleties of recommendation systems development, train a model on social network data and link it to our application.
Deep learning and neural networks allow solving problems in which classical models are powerless: face recognition, detection of objects in images, generation of meaningful text. We will look at popular neural network architectures, learn how to apply pre-trained models and train our own. We will build an advanced model and improve our recommendation algorithm.
Consider the basic concepts of probability theory and mathematical statistics. We will learn how to conduct A/B-tests and reliably assess the impact of ML models on the product and business. We will discuss the pitfalls of conducting experiments and ways of evaluating metrics in situations when it is impossible to conduct an A/B-test. We will implement our own testing system and find out whether we managed to improve the quality of recommendations in comparison with the basic solution.
We'll share our experience and tell you how interviews for Junior ML-engineers are conducted: we'll analyse algorithmic tasks in Python, as well as popular questions on machine learning, statistics and A/B tests. Practical tasks will help you gain confidence in your knowledge, build up your hand in advance and confidently pass this difficult stage.
TUITION FEE
Start mastering the ML engineering profession, get access to remote server work and support from our instructors.
> Python Application Development
> Machine Learning
> Deep Learning Fundamentals
> Statistics and A/B tests
> Machine Learning
> Deep Learning Fundamentals
> Statistics and A/B tests
> Preparing for interviews
> Support from teachers
> Work on a remote server
> Employment support
> Support from teachers
> Work on a remote server
> Employment support
$400
REGISTRATION ON THE EDUCATION PLATFORM
To pay for the course, you need to register on our education platform with your first name, last name and email.
If you already have an account, you can use it.
If you already have an account, you can use it.
ASK A QUESTION
We will contact you and answer any questions you may have about
the course.
the course.
STUDENT FEEDBACK /
FAQ
Yes, we carry out educational activities on the basis of a state licence.
For comfortable learning on the course you need to be able to write code in Python and compose SQL queries to databases. You will not need specialised knowledge of data engineering.
You can watch the lectures from any device, but you will need a computer or laptop to write code. There are no requirements for configuration and power - we will provide all the necessary infrastructure to work on a remote server. At the start of training, you don't need to install any special software - you will only need a browser and standard applications for communication: Telegram, Zoom, and Slack.
On average, our students study 10 hours a week. This is enough time to be able to watch lectures and complete homework on time.
We have organised the training in such a way that you can combine it with your work, study and personal life. You can study at any time and at a pace that suits you - all lectures are pre-recorded and broken down into short 15-30 minute videos, and there are soft two-week deadlines for homework.
The training lasts for 5 months. There will be three lessons each week, which will be accessed gradually. The lessons will consist of video lectures, notes and practical assignments that will take two weeks to complete. After the two-week deadline, access to the assignments will be granted. If you encounter difficulties during the course, you can seek help from mentors.
After each module there will be a week's holiday, during which you can catch up on the programme, finish your homework, read more about the topics that interest you, or simply relax.
ANY QUESTIONS?
Fill out the form, we will contact you, answer all your questions and tell you more about the course.
START IMMEDIATELY
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