This beginner-level course explores how to use generative AI, specifically ChatGPT, to streamline the process of creating product documentation. It covers setting up AI tools, generating comprehensive product content, and refining the AI-generated documentation to meet specific needs.
This course teaches how to apply AI to detect suspicious behavior and anomalies that signal potential security threats.
An advanced course on extracting information from text documents and constructing classification models. It covers feature vectorization, locality-sensitive hashing, stopword removal, and lemmatization.
Learn different techniques to build a model for anomaly detection specifically for time series datasets.
This course teaches you how to use image classifiers to perform object detection, recognition, and tracking using Tensorflow. By the end of this course, you'll have the skills and knowledge needed to create an image classifier.
This course provides a comprehensive introduction to data wrangling with the Pandas library in Python, covering essential data cleaning and transformation techniques.
Learn how to detect and respond to anomalies using Metricbeat and the Elastic Stack for enhanced enterprise monitoring.
This course introduces Elasticsearch, focusing on the basic building blocks of search algorithms and the underlying data structures. It covers installation, indexing, performing various types of search queries, and exploring the TF/IDF algorithm for search ranking and relevance.
This course on Pluralsight teaches how to train custom machine learning models on your own datasets using Google Cloud AutoML. It covers the underlying concepts of neural architecture search and transfer learning used by AutoML.
This course introduces learners to Vertex AI as a unified platform for building, training, and deploying AutoML machine learning models. It discusses the five phases of converting a use case to be driven by machine learning, emphasizing the importance of each step.
This course focuses on tuning Elasticsearch for low latency and high performance. It covers the index distribution architecture, cluster configuration, shards and replicas, similarity models, and advanced search techniques to improve the performance of search queries.
An introductory course to Power BI, a popular tool for creating interactive dashboards and visualizations for exploratory data analysis.
This course covers various feature engineering techniques to get the best results from a machine learning model, including feature selection (filter, wrapper, and embedded methods) and feature extraction from image and text data.
A beginner-level course teaching how to moderate text and image content for harmful or inappropriate material using Azure AI Content Safety. The course covers creating a Content Safety instance, performing text and image content moderation, and using prompt shields to detect indirect attacks.
A course on Pluralsight that covers the end-to-end process of time series forecasting in Python, from data exploration to model deployment.
Explore the use of system resource usage data to reveal advanced attacker techniques and uncover hardware supply chain interdiction.
This course teaches you how to use R to apply clustering, dimensionality reduction, and anomaly detection techniques to explore and analyze unlabeled datasets, including algorithms like k-means, hierarchical clustering, and DBSCAN.
This learning path introduces the practical use of AI and machine learning in cybersecurity, covering how AI enhances threat detection, anomaly detection, and automated incident response.
A learning path on Pluralsight that teaches how to apply common feature engineering techniques as part of the machine learning workflow specifically within the Microsoft Azure platform.
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