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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.
Explore innovative approaches to leveraging climate data for effective action. Learn about improving data access, treating environmental data as a public good, civic monitoring challenges, and accelerating building decarbonization through actionable insi…
Discover how to efficiently manage, process, and clean data using low-code tools. Learn to connect Apache Spark, Databricks, and TimeXtender for rapid data curation and confident decision-making.
Explore semi-supervised binary classification with Positive-Unlabelled learning for propensity measurement using PU bagging techniques.
Learn to forecast online sales demand using data science for accurate resource planning in fulfilment centres, reducing costs and improving delivery times.
Explore causal learning for data-driven marketing decisions in an era of privacy protection and unpredictable market conditions.
Explore strategies for training machine learning models on imbalanced datasets, including synthetic data generation, algorithm selection, and sampling techniques. Gain practical insights for tackling rare instance identification challenges.
Explore customer behavior analysis using topic modeling on retail basket data. Learn to identify shopping patterns and tag customers based on their purchasing habits.
Discover techniques to identify, assess, and proactively fix silent ML model failures. Learn to build reliable, compliant solutions resistant to performance degradation using AI Observability tools.
Explore causal inference and Bayesian techniques in retail, focusing on supply chain, customer behavior, and promotion effects at M&S. Learn to answer "why" and "what-if" questions using machine learning.
Explore the data engineering journey of CRM platform migration, focusing on personalization, cross-channel messaging, and offer optimization to enhance customer engagement and product development.
Develop customer analytics applications using Python and TIBCO Spotfire. Learn RFM segmentation, customer lifetime value, and market-basket analysis for actionable consumer insights across industries.
Discover why focusing on the last mile of analytics is crucial for improving decision-making and achieving better business outcomes in digital transformation initiatives.
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