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Machine Learning (Advanced)

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.

Earn Your Certificate

Complete all lessons, coding exercises, and pass all quizzes to earn a verified certificate with downloadable PDF and LinkedIn sharing.

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Course Modules

Intermediate
€20
Introduction to Deep Learning
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.
Machine Learning
Deep Learning
Neural Networks
2h 10m
10 lessons
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Intermediate
€20
Natural Language Processing (NLP)
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.
Machine Learning
NLP
Natural Language Processing
2h
11 lessons
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Intermediate
€20
Time Series & Forecasting
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.
Machine Learning
Time Series
Forecasting
2h
10 lessons
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Intermediate
€20
ML Interpretability and Explainability
Learn to interpret machine learning models through hands-on implementation of SHAP, LIME, GradCAM, and other essential techniques.
Machine Learning
Interpretability
Explainability
2h
11 lessons
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