Build advanced machine learning models to solve real industry challenges and fast-track your way to an elite tech career.
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The Machine Learning Engineering bootcamp is an intensive hands-on training program created for individuals who want to learn or upskill in machine learning and model deployment. A Machine Learning Engineer, is the person who designs, builds, monitors and deploys predictive models and AI systems. In simple words, the role of a Machine Learning Engineer combines Machine Learning with DevOps.
After successful completion of this bootcamp you will be ready to enter the job market as a machine learning engineer, with practical experience on machine learning (with the use of scikit-learn and keras), NLP, Neural networks, Deep Learning, MLOps, ML in Production, Time Series Analysis, Microsoft Azure and more.
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25% theory - 75% practice
During each day we spend max 25% of the time for theory (lecture, live coding, code reviews) in order to get you as fast as possible to the exercises, where the real learning and fun begins!
Real-world knowledge
Our instructors are professionals in the industry. With their real-world experience, they will help you learn practical and soft skills and build a strong foundation to begin your data science career.
Developed exclusively by Big Blue Data Academy, Sophie is an AI code agent integrated directly into your entire learning journey.
Companies Trusting Us
Industry Projects
Successful Graduates
Teaching Hours
Each module introduces new concepts, leading to the final project where you apply everything you've learned. This hands-on experience solidifies your new in-demand skills and demonstrates your ability to put them to use.
Learn SQL, make advanced queries and access data from databases.
Use APIs or web scraping to get data for your projects.
Work on Power BI, Plotly & Dash and MongoDB.
Tools that will be used:
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Learn all the Machine Learning algorithms used by the industry and use various optimization techniques to build your models. Get familiar with Artificial Intelligence.
Regression and Classification: Use algorithms like Random Forest, XGBoost for machine learning models optimization in action.
K-means clustering, Hierarchical clustering, Anomaly detection, Principal Component Analysis, Apriori algorithm, Singular value decomposition, t-sne, outlier detection.
Clustering and Labeling: Use K-Means, Hierarchical clustering, DS-SCAN to find common patterns, and Isolation Forest, Local Outlier Factor for outlier and anomaly detection.
Tools that will be used:
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Access and manipulate large amounts of text data from NoSQL servers (MongoDB).
Neural Networks and Convolutional Neural Networks (Keras) for image classification, using deep learning neural networks.
Classic NLP for sentiment analysis, topic modeling, and recommender systems.
Learn to build Large Language Models (LLMs) to master Generative AI (Chatbots).
Use forecasting algorithms (ARIMA and Prophet) for pattern detection, inferential statistics and forecasting.
Tools that will be used:
Final Project
Work on your final project solving a Machine Learning challenge. Get the opportunity to network with companies actively seeking Machine Learning Engineers during Final Projects presentation online event.
Mon-Wed-Thurs
18:00-22:00
Saturday
10:00-17:00
In class: Greek
Materials: English
14 weeks
280 hours
Μath, stats, Python, and Analytics background
Workshops on creating a standout CV and LinkedIn profile.
Mock technical interviews and support throughout the hiring process.
Link with industry professionals and companies to expand your network.
For more details about payment have a look at our FAQ.
Special discounts for companies and groups of individuals (2+ participants), contact us.
Fill in and submit your application. Tell us about yourself and your motivation to enroll.
We reach out to schedule an interview with a member of our team. We'll discuss your technical background and career goals, and provide details about the course.
We will invite you to a second interview with one of our Data Scientists to assess your programming skills and critical thinking through a discussion.
You can prepare yourself by watching the Introduction to Python for Data Science course on YouTube.
You're ready to embark on your Data Science journey. You'll receive the Prep-Work exercises to work on before your first day of the Bootcamp.
What is Machine Learning and why is it important?
Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It is vital because it allows organizations to automatically find hidden patterns and insights within massive, complex datasets that are impossible for humans to process manually. By transforming this data into actionable intelligence, ML powers predictive analytics, hyper-personalizes user experiences on platforms like Netflix and Amazon, and drives advanced automation in technologies like self-driving cars and fraud detection. Ultimately, it fuels innovation across major industries, significantly accelerating fields like healthcare through faster drug discovery and more accurate medical diagnostics.
What's the difference between a Machine Learning Engineer and a Data Scientist?
While a Data Scientist focuses on building predictive models and developing algorithms using advanced machine learning techniques, a Machine Learning Engineer primarily focuses on taking those models and putting them into production by building the software infrastructure and scaling the systems required to run them efficiently in the real world.
Where can a Machine Learning Engineer work?
A Machine Learning Engineer can work across various fields, including big tech, finance, healthcare, e-commerce, automotive, and entertainment. They apply their skills to roles focused on deploying predictive models, developing intelligent systems, and scaling algorithms for automation. These professionals use data to design core AI infrastructure, build recommendation engines, and automate complex decision-making processes across diverse industries.
What is the difference between our Machine Learning Engineering and Data Science Bootcamp?
Our Data Science Bootcamp provides you with 500 hours of all the vital knowledge and skills (Python, Analytics, Machine Learning, Deep Learning, Artificial Intelligence, NLP) to support your career in the Data Science field, while our Machine Learning Engineering Bootcamp provides you with 280 hours of intense machine learning focus and model deployment to prepare you for building and scaling AI systems in production.
Kickstart your career as a Machine Learning Engineer
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