What is Machine Learning Engineer?
A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.
Our course is focused primarly on using ML for building Artificial Intelligence systems.
Curriculum breakdown
Module 1: Introduction to Python and Data Management
During the first week, you will dive into the world of Python programming and learn how to effectively manage and manipulate data. This foundational knowledge is essential for your journey in data science. We will also explore data visualization techniques to present insights effectively.
Key topics covered:
- Introduction to Python programming
- Variables, data types, and control structures
- Data management with Pandas library
- Exploratory data analysis
- Introduction to data visualization using Matplotlib
Project 1: Exploratory Data Analysis. Apply Python and Pandas to load and explore a dataset, perform data cleaning, and generate meaningful visualizations.
Module 2: Mathematics and Probability for Data Science
In the second module we will strengthen your mathematical and statistical foundations, providing you with the tools needed to understand and implement various data science algorithms. You will gain insights into probability theory and hypothesis testing.
Key topics covered:
- Linear algebra for data science
- Calculus essentials for optimization algorithms
- Probability distributions and statistical inference
- Hypothesis testing and p-values
- Correlation and regression analysis
Project 2: Statistical Analysis. Apply statistical concepts to analyze a dataset, perform hypothesis testing, and draw meaningful conclusions.
Module 3: Classical Machine Learning Techniques
During this module you will dive into classical machine learning algorithms. We will explore the theory behind these algorithms and their practical implementations. You will gain a solid understanding of supervised and unsupervised learning techniques.
Key topics covered:
- Supervised learning: Linear regression, logistic regression, decision trees, and random forests
- Unsupervised learning: Clustering algorithms (k-means, hierarchical clustering)
- Model evaluation and validation techniques
Project 3: Predictive Modeling. Develop a predictive model using regression or classification algorithms to make accurate predictions on a given dataset.
Module 4: Deep Learning and Neural Networks with TensorFlow
In the fourth module we will delve into the exciting field of deep learning and its applications. You will gain a strong foundation in neural networks, TensorFlow, and key optimization techniques.
Key topics covered:
- Introduction to deep learning and neural networks
- TensorFlow basics and building neural networks
- Gradient descent and backpropagation algorithms
- Fine-tuning and optimizing neural networks
Project 4: Image Classification with Neural Networks. Build a neural network model using TensorFlow to classify images from a given dataset.
Module 5: Convolutional Neural Networks (CNN) and Computer Vision
During the fifth module we will focus on convolutional neural networks (CNNs) and their applications in computer vision tasks. You will learn to extract meaningful features from images and leverage CNNs for tasks like image classification, object detection, and segmentation.
Key topics covered:
- Introduction to convolutional neural networks (CNNs)
- CNN architecture and layers
- Image classification with CNNs
- Object detection and localization
- Image segmentation
Project 5: Object Detection with CNNs. Develop an object detection system that can detect and localize objects within an image using CNN-based techniques.
Module 6: Natural Language Processing (NLP) and Text Analysis
In this module we will explore the field of natural language processing (NLP) and its applications in text analysis. You will learn techniques to process and analyze textual data, perform sentiment analysis, and build models for text classification.
Key topics covered:
- Introduction to natural language processing (NLP)
- Text preprocessing and tokenization
- Sentiment analysis
- Text classification techniques
- Word embeddings and language models
Project 6: Sentiment Analysis. Develop a sentiment analysis model using NLP techniques to analyze and classify the sentiment of textual data.
Module 7: Advanced Deep Learning Techniques
In this module you will be introduced to advanced deep learning techniques including generative models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). You will learn how to generate new content and explore unsupervised learning with these models.
Key topics covered:
- Introduction to generative models
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Unsupervised learning with generative models
Project 7: Image Generation with GANs. Implement a GAN model to generate new images based on a given dataset, showcasing the ability to create realistic synthetic data.
Final Project and Portfolio Showcase
In the final module you will work on an independent final project where you can apply the knowledge and skills acquired throughout the course. This project will serve as a centerpiece in your portfolio, demonstrating your ability to solve real-world data science problems.
Key topics covered:
- Project planning and scoping
- Data exploration and preprocessing
- Model development and evaluation
- Documentation and presentation of the project
- Portfolio development for job readiness
Final project.
Develop a complete data science project from start to finish, incorporating various techniques and methodologies learned throughout the course. The project should demonstrate your ability to tackle real-world data challenges and deliver actionable insights.
Note: Each week will include additional resources, readings, coding exercises, and assessments to reinforce learning and provide feedback to students.
Your mentor
Vladimir Manaev

Vladimir is a dedicated educator specializing in data science and AI, committed to guiding students on their transformative journey. With over 10 years of industry experience and a strong background in financial data science, Vladimir brings practical insights and expertise to his teaching. His deep understanding of data-driven techniques, combined with his prestigious credentials, including MicroMaster's degree in Finance from MIT, equips him with the knowledge to empower aspiring data scientists.
Vladimir's passion for education is evident through his successful teaching experience at renowned institutions like the EU Business School and ESADE Business School.
Prerequisites
During this coruse you will need to work with Python, Math and Probability.
Python: First module includes an extensive video course on Python which starts from the basics and takes you to the advanced level. It contains about 7 hours of video lessons and around 100 of exersies with tests for you to practice. If you have programming experience with any language you are good to go. If you never did any programming we will suggest you some free resources where to begin.
Math and Probability:You will need to be comfortable with basics of Calculus, Linear Algebra and Descriptive Statistics.
Tuition
3950€
900€ paid upon registration, the remaining is due before course begins.
Scholarships
If you fall into one of these categories your tuition would be reduced by 500€
Students over 40: because we know it’s harder to commit to learning at a certain age and we are willing to help.
Women in tech: we're proud to say that 45% of our graduates are women, and we're committed to achieving full gender equality. This is especially significant given that less than 9% of women work in tech, according to StackOverflow's 2022 industry survey.
How to get a scholarship:
After signing up online you will get a student's form to fill out where you can select the applicable scholarship. Your final tuition quote would be calculated based on that and you will get links for making the remaining payments.
Financing
We can assist in getting a student loan which usually have lower rates than consumer loans.
To do so we can send you a proforma invoice for the selected course which you can attach to the student's loan application.
Please fill out this form.
Instalment payments are available at the checkout
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