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Machine Learning basis

Machine Learning basis

This module starts with an introduction to machine learning: how it is organized, what are the sub branches of machine learning, fundamental differences between these approaches and types of problems they are designed to solve.

Next, students get familiar with framing a machine learning problem, picking up appropriate objective function and algorithm according to a given problem. It is well known that data wrangling and feature engineering takes most of the time of model development. Students learn techniques to effectively deal with missing values, outliers, categorical variables and design new features.

Curriculum Overview

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Jul 27

Machine Learning Basis Registration

27.07-12.08

27 July 09:00 - 12:00
  • Formulating an ML problem;
  • Feature engineering;
  • Loss functions;
  • Generalization and performance estimation;
  • Hyperparameters optimization;
  • Model selection;
  • Linear regression;
  • Logistic regression.

Duration: 12 days

(Monday to Friday)

online

Fee: 7500 UAH

TOP skill you will learn:

  • Mathematical computing using popular Python packages as NumPy or Scikit-Learn
  • How to use linear/non-linear models
  • How to prepare your data for model building (feature engineering)
  • How to train and evaluate the performance of machine learning models
  • How to tune the model’s hyperparameters and select models

This is exactly for you if you are:

  • a person looking for a career change
  • a graduate from universities looking for a job in Data Science
  • a developer with a mathematical mindset who would like to get career growth
  • a business owner who would like to utilize data analysis and implement data-driven and AI projects
  • a Data Scientist practitioner who wants to systematize the knowledge and to master Deep Learning