Classes
Machine Learning 101

Subject: Information Technology

🧩 43 Practice Tests & Quizzes 📘 85 Study Guides
Introduction

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
 

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.


Latest Practice Tests / Quizzes
📝 Machine Learning 101 Practice Test: Neural Networks in Machine Learning
📝 Machine Learning 101 Practice Test: Naive-Bayes Algorithm
📝 Machine Learning 101 Practice Test: K-Nearest Neighbor Algorithm and Nearest Neighbor Analysis
Latest Study Guides
📄 Cloud ML - Google Cloud Professional Machine Learning Engineer: Vertex AI Agent Builder and Search & Conversation Overview
📄 Cloud ML - Google Cloud Professional Machine Learning Engineer: Translating Business Challenges into ML Problems (Feasibility, Success Metrics)
📄 Cloud ML - Google Cloud Professional Machine Learning Engineer: Training Options (Vertex AI Workbench, Custom Training, AutoML, BigQuery ML)
Exam Survival Guides
Survival guide for this class coming soon.