Machine learning. Basic - course 52,668 rub. from Otus, training 6 months, date February 27, 2023.

Miscellaneous / by admin / November 30, 2023

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You will learn to solve problems from real work processes, which are most often assigned to novice specialists in Data Science. By the end of the course, you will have collected a portfolio of work, completed interview preparation and career counseling.

The course will give you the necessary foundation:
Python. You will go through the basics of programming and learn how to use this most relevant language in Machine Learning tasks.
Mathematics. Master key sections to understand the theoretical foundations and principles of algorithms.
Classic Machine Learning models. Collect your data sets and complete a full pipeline of work with your first models.

Creative atmosphere:
During training, you will be immersed in conditions close to real work processes. You'll have to deal with dirty data, think ahead, experiment with solutions, and prepare models for production.
The classroom environment encourages students to be curious, actively discuss, and not be afraid of making mistakes.

Personal mentor:
Online sessions for 40 minutes every week;

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At the beginning of your training, you are assigned a mentor. Like teachers, mentors are experts working in Data Science;
Once a week you do your homework, post it on GitHub and arrange a call with your mentor;
The mentor gets acquainted with your code in advance, so by the time of the meeting he already knows what to pay attention to. You can also prepare questions;
During the session, the mentor will comment on your decision. If necessary, you can immediately go to the development environment, make changes to the code and immediately see the result.

After training you will be able to:
Apply for positions that require junior competencies
Solve real business problems using machine learning methods
Work with Python libraries for Machine Learning
Coping with non-standard situations through a deep theoretical understanding of how algorithms and models work
Navigate in various areas of Data Science and select tools suitable for the task.

Maria Tikhonova

course

Works as a data analyst in the AGI NLP team in Sberbank. Works on neural network language models and their application in real-life problems. Believes that working in the field of Data Science provides a unique...

Works as a data analyst in the AGI NLP team in Sberbank. Works on neural network language models and their application in real-life problems. He believes that working in the field of Data Science provides a unique opportunity to do crazy cool things on the edge of science that are changing the world here and now. Teaches subjects in data analysis, machine learning and data science at the Higher School of Economics. Maria graduated from the Faculty of Mechanics and Mathematics of Moscow State University and the Yandex School of Data Analysis. Maria is currently a graduate student at the Higher School of Economics at the Faculty of Computer Science. Her research interests include data science areas such as natural language processing and topic modeling. Program Manager

Dmitry Sergeev

course

Practicing machine learning and data analysis since 2012. Currently working as Head of R&D at WeatherWell. Has experience in the practical application of machine learning in game development, banking and Health Tech. He taught machine learning and data analysis at the Center for Mathematical Finance of Moscow State University, and was a guest lecturer at the Faculty of Computer Science of the National Research University Higher School of Economics and various summer schools. Education: Economics-mathematics REU im. Plekhanov, Central Faculty of Mathematics and Mathematics of Moscow State University, advanced professional training of the Faculty of Computer Science of the Higher School of Economics "Practical data analysis and machine learning", MSc Computer Science Aalto University Stack/Interests: Python, Machine Learning, Time Series, Anomaly Detection, Open Data, ML for social good

Introduction to Python
-Topic 1.Getting to know each other
-Topic 2. Setting up the work environment
-Topic 3.Basic types and data structures. Flow control
-Topic 4.Working with functions and data
-Theme 5.Git, shell

Introduction to Python. OOP, modules, databases
-Topic 6. Fundamentals of OOP
-Topic 7.Advanced OOP, exceptions
-Topic 8.Advanced OOP, continued
-Topic 9.Modules and imports
-Topic 10.Tests
-Topic 11.Introduction to built-in modules
-Topic 12. Files and network

Python Basics for ML
-Topic 13. NumPy Basics
-Topic 14. Pandas Basics
-Topic 15.Data visualization

Theoretical minimum for ML: mathematics, linear, statistics
-Topic 16.Matrixes. Basic Concepts and Operations
-Topic 17.Practice. Matrices
-Topic 18. Differentiation and optimization of functions
-Topic 19.Practice. Differentiation and optimization of functions
-Topic 20. Algorithms and computational complexity
-Topic 21.MNC and MSE
-Topic 22.Practice. MNEs and MSEs
-Topic 23. Random variables and their modeling
-Topic 24.Practice. Random variables and their modeling
-Topic 25. Study of dependencies: nominal, ordinal and quantitative quantities
-Topic 26.Practice. Study of dependencies: nominal, ordinal and quantitative quantities
-Topic 27.AB testing

Basic Machine Learning Methods
-Topic 28.Introduction to machine learning
-Topic 29.Exploratory Data Analysis and Preprocessing
-Topic 30. Classification problem. Nearest neighbors method
-Topic 31.Regression problem. Linear regression
-Topic 32.Logistic regression
-Topic 33.Decision trees
-Topic 34.Feature engineering & advanced preprocessing
-Topic 35. Practical lesson - solving Kaggle using everything we’ve learned

Project work
-Topic 36. Selection of topic and organization of project work
-Topic 37. Project consultation
-Topic 38.Project protection

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