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Machine Learning - course RUB 39,240. from SkillFactory, training 12 weeks, Date August 13, 2023.
Miscellaneous / / December 02, 2023
What does the course consist of?
The course includes 10 modules, more than 500 exercises to reinforce the material, training in 10 machine learning algorithms, 2 hackathons on kaggle, chat with the community and mentor support
The Data Science specialization consists of courses:
Python
Math&Stat
Machine Learning
Deep Learning
Data Engineering
Management
Skill training
Each topic is covered in videos, screencasts and notes and reinforced with dozens of exercises (tests, code debugging, checking student code).
Community and mentor
During the course, you will not be left alone with difficulties - not only your classmates will help you, but also the course mentor.
Model training
In the course on each topic, you work with the ML model - fine tune, create from scratch, optimize, try different methods.
Introduction to Machine Learning
— We get acquainted with the main tasks and methods of Machine Learning, study practical cases and apply the basic algorithm for working on an ML project
— We solve 50+ problems to consolidate the topic
Data preprocessing methods
— We study data types, learn to clean and enrich data, use visualization for preprocessing and master feature engineering
— We solve 60+ problems to consolidate the topic
Regression
— We study data types, learn to clean and enrich data, master linear and logistic regression, study the limits of applicability, analytical inference and regularization
— Training regression models
— We solve 40+ problems to consolidate the topic
Clustering
— We master learning without a teacher, practice its various methods, work with texts using ML
— We solve 50+ problems to consolidate the topic
Tree-based algorithms: an introduction to trees
— Getting acquainted with decision trees and their properties, mastering trees from the sklearn library and using trees to solve a regression problem
— We solve 40+ problems to consolidate the topic
Tree-based algorithms: ensembles
— We study the features of tree ensembles, practice boosting, use the ensemble to build logistic regression
— We solve 40+ problems to consolidate the topic
— We are participating in a competition on kaggle for training a tree-based model
Assessing the quality of algorithms
— We study the principles of sample splitting, under- and overtraining, evaluate models using various quality metrics, learn to visualize the learning process
— We evaluate the quality of several ML models
— We solve 40+ problems to consolidate the topic
Time series in machine learning
— Getting acquainted with time series analysis in ML, mastering linear models and XGBoost, studying the principles of cross-validation and parameter selection
— We solve 50+ problems to consolidate the topic
Recommender systems
— We study methods for constructing recommender systems, master the SVD algorithm, evaluate the quality of recommendations of the trained model
— We solve 50+ problems to consolidate the topic
Final hackathon
— We apply all the studied methods to obtain maximum accuracy of model predictions on kaggle
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