This course will teach you how to build a system for sentiment classification. You'll learn the internal intricacies of Recurrent Neural Networks and implement a sentiment classifier using an open-source Amazon product review dataset.

In this module, you'll learn how to set up and configure an x86-based Ubuntu 18.04 system to host an NVIDIA DeepStream development environment.

Explore techniques and best practices for machine learning with small datasets.

A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.

Learn to solve real-world problems by building a customer support app using Large Language Models (LLMs). Discover how to enhance user interactions and automate customer service tasks with AI.

This course will teach you how to handle geospatial data and find the shortest possible route between coordinates by car using Python.

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Baue Schritt für Schritt Deinen eigenen Sprachassistenten mit Python

First-order logic is a restricted, formalized language which is particularly suited to the precise expression of ideas. The language has uses in many disciplines including computer science, mathematics, linguistics and artificial intelligence.

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (AWS) - Deploying AI & ML Models in Production using Amazon Web Services.

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (Azure) - Deploying AI & ML Models in Production using Microsoft Azure Machine Learning