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Explore clustering in machine learning, focusing on k-means and hierarchical algorithms. Learn to uncover data relationships and apply techniques with practical considerations.
Explore classification techniques in machine learning, including logistic regression and KNN, with practical insights from industry experts.
Explore regression in machine learning: estimate relationships, predict outcomes, and apply algorithms for continuous target variables. Learn evaluation techniques and practical considerations.
Explore core linear algebra concepts in data science, from vector representations to matrix factorization, with real-world applications in text similarity, image processing, and content recommendation.
Explore graph databases, their comparison to relational databases, and ideal use cases. Learn about property graphs and gain resources for further study in this informative introduction.
Learn Git fundamentals for source control in software development, including key concepts and commands demonstrated through practical examples.
Explore data using Excel: Learn importing, cleaning, basic functions, and statistical analyses for valuable insights from a Mars Petcare Data Engineer.
Gain foundational knowledge of databases, their structure, and applications in solving real-world problems. Explore data types, analytics cubes, and structured data formats.
Learn effective data visualization techniques to represent insights concisely, choose appropriate charts, and craft compelling data stories for impactful communication.
Explore computer vision using CNNs and transfer learning. Learn practical steps for CNN training, including activation functions, and discover applications in image quality assessment and feature extraction.
Explore causal relationships in data beyond correlation. Learn to ask "Why?" and unveil insights using simple techniques, avoiding misinterpretation pitfalls like Simpson's Paradox.
Learn best practices for scaling data science projects from local to production, covering infrastructure, reliability, and real-world applications in data engineering.
Explore Orbit, a probabilistic forecasting tool developed by Uber, and learn its application in demand forecasting for technical support teams through a practical case study.
Explore methods for automating the labelling of large NLP datasets, from traditional Support Vector Machines to cutting-edge few and zero-shot learning techniques.
Explore automated causal inference techniques using auto-causality package. Learn to analyze A/B test results, measure impacts, and identify customer responses to different options.
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