Advanced level

Remote

Machine Learning course (with Python)

Live classes during evening hours

2 months, 2 times per week

Up to 15 students per class

Course materials and classes are in English

Seize the modern day with our practical Machine Learning course that emphasizes supervised learning and neural networks.

This course is perfect for math and code lovers — or anyone passionate about cutting edge technology. By the end of it, you will be able to understand the fundamentals of machine learning, implement its algorithms from scratch using Python libraries, and build and train neural networks to solve real-world problems. Your final project is exactly that — a deployed machine learning model for a system or idea of your choice.

Admission requirements

Some programming experience, ideally in Python

but solid knowledge of the fundamentals in any programming language is enough. So if you’ve coded before, you’ve got this one down. Feeling unsure? Check out our Python development course and consider it first.

Being comfortable with high-school mathematics

as this course will contain derivatives, matrix and vector operations, etc. You don’t need to be a calculus expert, but to get the most out of this course, you should be able to follow along with the foundations. Revise some math materials online or dust off your high-school workbook — and you’ll be ready in no time.

Intermediate English and above.

The place of Machine Learning

Being the “brain” behind artificial intelligence (AI), machine learning enables computers to learn from data and make smart decisions without explicit instructions. Its adoption by businesses across all industries is ever increasing — and so is the need for Machine learning engineers.

According to the World Economic Forum, the demand for AI and Machine learning specialists is expected to grow by 40% from 2023 to 2027. On LinkedIn, there are currently over 100,000 Machine learning engineer vacancies worldwide, with the average salary for Juniors sitting at 68,000 EUR a year.

Intrigued? Let’s take a look at your skill set after studying Machine Learning at Beetroot Academy.

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Your future skillset

Role:

Trainee / Junior Machine learning engineer

strong understanding of foundations of machine learning approaches and algorithms

Experiunderstanding of neural networksence with Git and GitHub

practical experience of using deep learning frameworks in real projects (preferably TensorFlow (+Keras), PyTorch)

experience with Natural Language processing (NumPy, Spacy)

ability to work machine learning tools (NumPy, Pandas, SciKit Learn)

awareness of machine learning trends

knowledge of basic data structures and algorithms

knowledge of Big Data frameworks (Spark, Hadoop, Kafka, ElasticSearch, etc) 

understanding of machine learning techniques like Linear Regression, Decision Trees, SVM, kNN, Random Forest

proficiency and deep understanding of algorithms in ML/DL

experience with image recognition (OpenCV, OCR or alternative) 

Modules

Machine Learning Fundamentals

12 hours

6 topics

Get to know everyone. Introduction to the field of machine learning and the programming environment used in the course (Jupyter notebooks, Python libraries, etc.)

Linear regression with multiple features

Regularization

Linear regression

CSS Animation

Logistic regression for binary classification

Classification with multiple classes

Classical Methods and Neural Networks

8 hours

4 topics

Tree algorithms with the scikit-learn and xgboost libraries

Recommender systems

Introduction to unsupervised learning: clustering, anomaly detection, dimension reduction

Neural networks fundamentals

Deep Neural Networks

16 hours

8 topics

TensorFlow and Keras python libraries. Introduction to Google Colab

Popular image recognition networks and using them to perform transfer learning

Introduction to natural language processing and recurrent neural networks

ML Ops overview. Deploying a TensorFlow/Keras model via tensorflow-serving and Docker

Convolutional neural networks for image recognition

Image-based object detection

Transformers for natural language processing

React: Course wrap-upHooks

Bonus Module. Generative AI, Advanced Methods, and AI Ethics

6 hour

3 topics

Generative AI basics

Advanced machine learning methods

Ethical challenges in AI

Course author

Tomas Bengtsson

7+ years in IT
Software Developer у Skira

Worked for Volvo Group Trucks Technology in the department for vehicle automation

Experience in the field of self-driving vehicles, about the sensors that are responsible for detecting pedestrians, vehicles of different types, and other objects of interest

Ph.D. in Chalmers University of Technology, Sweden

Course consultant

Igor Vustianiuk

6+ years in IT
Data Scientist/Python Developer at Beetroot

Our teachers

All teachers pass multiple interviews to ensure aligning values, as well as impeccable soft and technical skills. The final step is a demo class where they teach a topic to other teachers.
‍
Our team of methodologists supports and trains them continuously. This is the place where industry experts become great teachers.

But there’s more!

Aside from providing industry-leading education, we ensure that you receive meaningful support from student success managers and peers.

Like-minded students

We strive to ensure alignment in our students’ determination and values. We find this to an absolutely essential for an optimal learning experience.

Student success managers

Every group has a dedicated manager who will make sure that your course goes as planned. Think of them as your new best friend who can answer any questions you may have.

Community access

After graduating, you’ll join our extended family and become part of the alumni network. You'll have access to news, events, job opportunities, and many other perks.

Як ми навчаємо

Ми надаємо більше, ніж просто якісну освіту. Beetroot Academy підтримує своїх студентів на кожному етапі. Ось як:

Перевернутий клас

Вивчай теорію вдома та зосередься на практиці на заняттях.

Живі заняття

Навчайся в Zoom з експертом галузі — до 18 студентів у класі та у вечірній час.

Підтримка

Отримай необхідну допомогу, щоб розпочати нову кар’єру.

Софт-скіли

Навчайся в командах під керівництвом викладача як тімліда.

Працевлаштування

Отримай інструменти та знання, необхідні для першої співбесіди.

Глобальна спільнота

Стань частиною екосистеми Beetroot. Це випускники, ІТ-компанії та партнери по всьому світу.

How we teach

In the 9 years of our work, over 12,000 students have trusted the Academy and joined 700+ companies worldwide after completing our courses. But not only numbers speak of our services. We're honored to be accredited by Almega, a European organization acknowledging education providers. We meet the Swedish quality standards for adult education, and here's what we offer:

Live classes with a teacher

Study theory in our LMS anytime and focus on practice during live classes led by an industry expert.

Personal career coaching with a professional recruiter

Craft a standout CV & LinkedIn profile, learn to ace interviews, and develop a personal job search and development strategy. Get the 6-month support you need to launch your tech career.

Course curriculum from senior experts

Our courses are created and updated by a group of senior specialists who also teach at the Academy outside of work. They ensure our program always meets the market requirements.

Cozy community in the classroom

We teach in small groups of up to 15 people, so you’ll add teamwork to your skillset, too. Help others, get help yourself, and build your soft skills from day one of studying.

Support of the group coordinator

Your coordinator will have your back throughout the course and will help you stay motivated and organized. Their goal is to make your education as comfortable and effective as possible.

Admission process

Step 1

Entry test

Step 2

Talk to us

After successful test results, you’ll have a chat with our educational advisor

Step 3

Admission

If we're a good match, prepare for intensive studies and start your dream career in Tech

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Antonia Massara

Tour operator → UX designer

Mikaela Söhnchen

Self-employed consultant → UX-Lead

Pavlo Bondarchuk

Shop assistant → Scrum master

Natálie Baranová

Administrative Assistant → Junior QA Manual

Antonio Lupu

Student → React Native Developer (Internship)

Nawara H. Hagentoft

Publishing field → UX writer

Olena Dolhusha

Philologist → Project Manager

Our graduates’ stories

Beetroot Academy students prove it is possible to launch a new career in under a year, but getting started can be the hardest. Get inspired by the stories of our graduates and begin your own journey today.

Explore stories

Your tech career starts here

Sign up for the course and go through all the admission stages to embark on a new career journey in less than half a year.

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FAQ

What exactly is machine learning?

We can think of it as finding patterns in data. When you develop a machine learning method (also called algorithm), it can, for example, predict the price of a house based on its size, the number of rooms, and other properties. This course is focused on so-called “supervised learning”, where we “show” the learning algorithm a lot of data samples: say, prices of different kinds of houses. In other words, our application finds patterns in data we provide and learns to recognize them in other settings.

Why is everybody buzzing about AI?

There are several factors that make our modern day a prime time for AI. Over the past 60 years, processing power has increased by a trillion-fold, so our computers are faster and can run more complex algorithms. Also, the cost of data storage and processing has lowered while businesses are collecting more data than ever before. That’s why AI is actively used to improve consumer apps and products, and this demand is driving further innovation and research.

What are some career opportunities in machine learning?

Machine learning is already something you use every day, perhaps without knowing it — for example, when doing a web search, or when your email application detects and removes spam for you. There is a large, unfulfilled demand for the machine learning skill set. Once you learn how to implement machine learning models, you can work on projects about personalized product recommendations; self-driving vehicles, speech/language recognition, personalized medication, and much more. Chances are, there will be machine learning positions in any field that sparks your interest.

How does machine learning differ from traditional programming?

Machine learning automates the process of learning from data to generate solutions, while traditional programming requires the programmer to manually specify the steps to solve a problem. Machine learning is often better suited for complex problems that involve identifying patterns in large datasets, such as predicting customer behavior or detecting fraud. Traditional programming may be better for problems that require precise control over the logic and functionality of a program, such as developing a video game or operating system.

What if I don’t know how to code or do advanced math operations?

Our main idea behind this course is to satisfy the curiosity of those already working within technology, IT, engineering, etc. If you don’t fall into this category and have no coding experience, you would need to take a programming course — be it self-paced or teacher-led. The best choice of programming language is definitely Python, so we recommend starting there. As far as math goes, there are many free resources covering the basics of linear regression or matrix-vector operations. Look up the topics of the first module and get comfortable with the concepts. You don’t need a PhD in mathematics to do machine learning!

What kind of project work is included in this course?

There are two projects in the course. At the end of module 2, you will be implementing a neural network from scratch using numpy (a Python library). The final project at the end of the course is about deploying a keras model to make it available for use by some application. It is a creative outlet as you’re free to choose the system you’ll be developing the model for — maybe you already have some unmet demand at your workplace or a fresh idea you’d like to bring into the world. This project is meant to be open-ended, so you can keep working on it as much as you need after the course is over.

Where can I find testimonials about the Academy?

Explore our graduates' reviews on popular feedback platforms like Course Report and Trustpilot. Also, find out more about their experience on our website.

How do you keep your courses relevant?

To ensure our courses remain up-to-date and relevant to the ever-evolving tech industry, we update our curriculum on a regular basis.We also employ industry experts as instructors, ensuring that our students receive the latest insights and tools from professionals who are actively shaping the tech landscape. In addition to our internal updates, we also actively seek feedback from our students and teachers.

Will I receive a certificate of completion?

Yes, your digital certificate will look like this. Our graduates add certificates to LinkedIn and other social networks to verify their skills. But most importantly, you will gain practical knowledge directly from a middle+ level specialist, create a portfolio, improve your soft skills by working side by side with other students, and prepare your CV, cover letter, and LinkedIn profile for job search in a new field.

Machine Learning course (with Python)

Admission requirements

The place of Machine Learning

Your future skillset

Modules

Our teachers

Career support

Як ми навчаємо

How we teach

Admission process

Our graduates’ stories

Your tech career starts here

FAQ