Study techniques for data segmentation and clustering to uncover patterns and insights in datasets.

More complex models often can be manually customized to improve how effective they are. Through exercises and explanatory content, we explore how altering the architecture of more complex models can bring about more effective results.

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.

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

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 (GCP) - Deploying AI & ML Models in Production using Google Cloud Platform

What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions.

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.