diff --git a/src/data/roadmaps/ai-data-scientist/content/machine-learning@kBdt_t2SvVsY3blfubWIz.md b/src/data/roadmaps/ai-data-scientist/content/machine-learning@kBdt_t2SvVsY3blfubWIz.md index fdbf93dc4..04ad4c37e 100644 --- a/src/data/roadmaps/ai-data-scientist/content/machine-learning@kBdt_t2SvVsY3blfubWIz.md +++ b/src/data/roadmaps/ai-data-scientist/content/machine-learning@kBdt_t2SvVsY3blfubWIz.md @@ -1,3 +1,9 @@ # Machine Learning -Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. \ No newline at end of file +Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. + +Learn more from the following resources: + +- [@article@Advantages and Disadvantages of AI](https://towardsdatascience.com/advantages-and-disadvantages-of-artificial-intelligence-182a5ef6588c) +- [@article@Reinforcement Learning 101](https://towardsdatascience.com/reinforcement-learning-101-e24b50e1d292) +- [@article@Understanding AUC-ROC Curve](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5) \ No newline at end of file