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Structured courses for deeper proficiency, made from standalone modules that are also available individually.
Showing 13 results
A guided progression to mastery
Beginner track
This course establishes essential machine learning foundations for professionals entering the field. You'll gain hands-on experience with core algorithms, data preparation techniques, and model evaluation methods used in real-world applications. ### Course Content * **Introduction to Machine Learning & Python for ML**: Master supervised and unsupervised learning fundamentals while building proficiency with Python libraries including NumPy, Pandas, Matplotlib, and Scikit-learn. * **Data Preprocessing & Feature Engineering**: Learn to clean and transform real-world data, handle missing values and outliers, encode categorical variables, scale features, and engineer meaningful attributes that improve model performance. * **Classical Machine Learning Algorithms**: Build and compare foundational models including linear/logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors, understanding when to apply each algorithm. * **Model Evaluation & Optimization**: Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well. ### Audience Analysts, software developers, and professionals with basic Python knowledge who want to transition into data science and machine learning roles.
Beginner · 2 months · Self-paced · 4 modules · 44 lessons
Certification from €80
View pathRecommended next → Machine Learning (Advanced)
Intermediate track
This course covers advanced machine learning techniques used in industry settings. You'll move beyond foundational concepts to work on complex problems that require specialized skills. ### Course Content - **Deep Learning:** Understand neural network architectures and configure them for specific tasks - **Natural Language Processing:** Build text analysis systems including chatbots, sentiment analysis tools, and document classifiers - **Time-Series Forecasting:** Develop models to predict trends, demand patterns, and behavioral changes - **Explainable AI:** Learn methods to interpret model decisions and communicate results to stakeholders - **Applied Project:** Complete a capstone project that demonstrates end-to-end ML implementation ### Audience Data analysts, junior data scientists, and software engineers seeking to develop advanced ML skills.
Intermediate · 2 months · Self-paced · 4 modules · 42 lessons
Certification from €80
View pathRecommended next → Retrieval Augmented Generation (RAG)
Advanced track
Language models answer questions about documents they have never seen when a retrieval step gives them the right passages. This path covers the full RAG pipeline, from chunking and vector search to hybrid retrieval, grounded answers and evaluation. It then adds knowledge graphs with GraphRAG, for questions that connect several facts or cover a whole collection of documents.
Advanced · 1 month · Self-paced · 2 modules · 22 lessons
Certification from €100
View pathThe building blocks of the certification paths
Master the fundamentals of supervised and unsupervised learning while building proficiency with essential Python libraries like NumPy, Pandas, and Scikit-learn for data manipulation and modeling.
The building blocks of a RAG system. The module covers document parsing and chunking, embeddings and vector search, hybrid search and reranking, query transformation, grounded answers with citations, and the evaluation of retrieval and generation.
Master deep learning fundamentals and apply them to real-world data across domains like computer vision and NLP through hands-on projects with industry-standard frameworks.
Retrieval over a knowledge graph built from documents. The module covers knowledge graph design, the extraction of entities and relations with a language model, community detection and summaries, local and global graph search, and the evaluation and cost of GraphRAG.
Learn to clean and transform real-world data, handle missing values and outliers, encode categorical variables, scale features, and engineer meaningful attributes that improve model performance.
Build strong foundations in Natural Language Processing, from classical methods to deep learning architectures, including embeddings, RNNs, and the basics of modern large language models.
Build and compare foundational models including linear/logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors, understanding when to apply each algorithm
Build expertise in time series forecasting fundamentals from decomposition to ARIMA models, then advance to machine learning and recurrent neural networks through hands-on projects with real-world datasets.
Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well.
Need something more hands-on?
Interview prep and team training are handled as dedicated services.