Daten-, KI- und Cloud-Kurse

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.

Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

Learn to streamline your machine learning workflows with tidymodels.

Master RAG with Weaviate! Embed text and images for retrieval, and experiment with vector, BM25, and hybrid search.

In this course youll learn techniques for performing statistical inference on numerical data.

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Build SQL skills by writing AI prompts that generate queries for sorting, grouping, filtering, and categorizing data.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Automatisiere die Datenbearbeitung mit KNIME, indem du Zusammenführungen, Aggregationen, Datenbank-Workflows und erweiterte Dateiverarbeitung beherrschst.

Nutze die Überlebensanalyse, um mit Zeit-zu-Ereignis-Daten zu arbeiten und die Überlebenszeit vorherzusagen.

Learn how to pull character strings apart, put them back together and use the stringr package.

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Learn to set up a secure, efficient book recommendation app in Azure in this hands-on case study.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn the bag of words technique for text mining with R.

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Learn how to design, automate, and monitor scalable forecasting pipelines in Python.

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.