Intro to Machine Learning

June 15 – August 14 10:00 – 15:00

Online: 41600 uah (10 Seats)

Offline: 52000 uah (15 Seats)


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Intro to Machine Learning

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

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

Curriculum Overview

Module 1: Intro to Machine Learning and Pre-Training Phase

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.

Topics covered:

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

Module 2: 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.

Topics covered:

  • k Nearest Neighbours
  • Tree-based models
  • Ensemble methods
  • Adaboost
  • XGBoost
  • Support Vector Machine (SVM)
  • Introduction to neural networks
  • Recommendation systems
  • Collaborative filtering

Module 3: Unsupervised Learning

Most of the time values of the target variable are unknown and that is when unsupervised learning techniques are needed. They enable us to identify hidden structures in multidimensional datasets.

Topics covered:

  • Principal component analysis (PCA)
  • k-means clustering
  • Hierarchical clustering
  • Anomaly detection

Module 4: Final Project

To test and assess students’ knowledge each of them picks a machine learning problem after completing all modules and tries to find a solution by going through the data preparation, model training and testing phases.

Module 5: Bonus Topics *

This module will introduce some advanced topics and instruments for data analysis that will boost and enhance your knowledge in Data Science.

  • Data Science project workflow
  • Business Intelligence (BI) systems
  • Model deployment
  • Pipelines
  • Excel
  • Time series analysis
  • Google Analytics
  • Introduction to e-commerce

Full price: ₴52000


Do you know that by choosing our course you will not only master Data Science but also save money?

Let’s count a little

The total is 225 working hours at a cost of $ 2000. So, you only pay $ 9 per hour.

It’s a bargain!

Have you seen such prices anywhere? Of course not! And we also offer part payment.

Everything is for your learning.

Our instructors

Iryna Lazarenko

One of the Lead Instructors at DEVrepublik boot camp and PhD in Mathematics
Iryna is a Senior Lecturer at the Department of Mathematical Modeling for Economic Systems at the National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute. She is also a Scientist at the Laboratory for computer modelling and intelligent data analysis at the World Data Center for geoinformatics and sustainable development
Her research interests are Data Analysis, Data Mining, Operation Research, Optimal Control, Sustainable Development, Theory of Integral and Differential Equations.
She loves travelling a lot.

Ivan Luchko

Ivan is one of the Lead Instructors at DEVrepublik.
Won a Bronze Medal in International Physics Olympiad, Mexico. Received a Master degree in Applied Physics at Technical University of Munich. Later performed some research in the Heisenberg antiferromagnets using Qauntum Monte Carlo simulations and machine learning technics in Geneva (and PSI), Switzerland. Has a diverse experience in business process automation, advanced analytics, mathematical modeling, optimization and machine learning. During the last two years Ivan works as Data Science team lead at Boosta. Recent projects are related to dynamic price optimization and recommendation system in e-commerce.
Loves sport, traveling and hiking.

Ruslan Klymentiev

Ruslan is a curriculum writer at DEVrepublik and Practice Instructor.
He graduated from Odessa National Polytechnic University, a specialist in "Radio-electronic devices."
2.5 years of experience in Data Science.
Interests: statistics, Data Visualization, CNN models and Computer Vision
2 times received at Weekly Kernels Award on
Hobbies: climbing and hiking

Nikolay Meretskiy

Nikolay is one of the leading instructors at DEVrepublik.
He has 4 years of experience in Data Science and machine learning using algorithms for predictive modeling, data processing, image and video processing, as well as data mining for solving complex business problems.
Nikolay successfully applies machine learning methods in medicine, finance, agriculture, AR and many other areas.
In addition, he worked with a large number of popular technologies, such as: Python, TensorFlow, Scikit, Keras, SciPy.
Hobbies: Hiking.