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Machine Learning basis

Machine Learning basis

Machine Learning is a first-class ticket to the most exciting careers in data today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

Our nine-week introductory course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning.

The course is both theoretical and practical, and we will ensure you understand the theory behind the algorithm before this is tested on real-world data examples. We will guide you through areas such as analyzing large amounts of data and classifying this into appropriate categories, and how to recognize recurring features and identify correlations, so that you can develop a complex system which has the ability to make accurate predictions.

You’ll look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skills using hands-on tasks. Get ready to do more learning than your machine!

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

Curriculum Overview

18  May – 3 June

09:00-12:00

(Monday to Friday)

online

Fee: 7500 UAH

Next events

Oct 05

Machine Learning Basis Registration

05.10-21.10

5 October 18:00 - 21:00
Nov 09

Machine Learning Basis Registration

09.11-27.11

9 November 09:00 - 12:00
Nov 23

Machine Learning Basis Registration

23.11-09.12

23 November 12:00 - 15:00
Nov 30

Machine Learning Basis Registration

30.11-16.12

30 November 18:00 - 21:00
Dec 21

Machine Learning Basis Registration

21.12-16.01

21 December 09:00 - 12:00
Jan 21

Machine Learning Basis Registration

21.01-08.02

21 January 12:00 - 15:00
Jan 23

Machine Learning Basis Registration

23.01-09.02

23 January 18:00 - 21:00

Intro to Machine Learning

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.

  • Formulating an ML problem;
  • Feature engineering;
  • Loss functions;
  • Generalization and performance estimation;
  • Hyperparameters optimization;
  • Model selection;
  • Linear regression;
  • Logistic regression.

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

Our instructors