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Supervised Learning

Supervised Learning

This course covers algorithms that are used when the target variable which has to be predicted is known. It starts with simple KNN and ends with fully connected feed-forward neural networks. Proper testing of a model is essential to build a reliable product. Students are introduced to various testing methods and parameters that help to build generalizable and stable models.

Curriculum Overview

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Aug 17

Supervised Learning Registration

17.08-07.09

17 August 09:00 - 12:00
  • k Nearest Neighbours;
  • Tree-based models;
  • Ensemble methods;
  • Adaboost;
  • XGBoost;
  • Support Vector Machine (SVM);
  • Introduction to neural networks;
  • Recommendation systems;
  • Collaborative filtering.

Duration: 3 weeks

(Monday to Friday)

online

Fee: 7500 UAH

TOP skill you will learn:

  • Expertise in mathematical computing using popular Python packages as NumPy or Scikit-Learn
  • Understand and use linear/non-linear models
  • Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, SVM, clustering and K-NN
  • Get understanding about how the magic of neural networks actually works and will be able to write them yourself
  • Build reproducible machine learning pipelines
  • Experience applying these methods to real-world problems
  • Experience of building machine learning model APIs

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