Machine Learning algorithms
What is Machine Learning?
Machine learning is a form of artificial intelligence that gives systems the ability to learn and improve automatically, without needing human input. It focuses on developing computer programs that have the ability to access data and use it to learn for themselves. The first step of learning is data or observations (i.e. instructions, examples, experience) so that patterns in data can be found. This can then make better decisions in the future. The overall goal is for the computers to learn automatically, without human input or assistance.
Why is Machine Learning important?
Machine learning has many practical features that can help fulfil business goals; for example, it can save on time and money. It essentially allows employees to get things done quicker, thus increasing productivity and efficiency. It also automates tasks that would otherwise be performed via human input, which frees up valuable time that can be used more efficiently elsewhere, where human effort is the only way of performing the task such as customer service.
What are the Machine Learning methods?
There are several machine learning methods that are often categorized as supervised or unsupervised. It is essential to know the strengths and weaknesses of each ones so that you can choose the right algorithm that is most beneficial to your business.
Here are some of the popular machine learning methods:
• Supervised machine learning algorithms
• Unsupervised machine learning algorithm
• Semi-supervised machine learning algorithm
• Reinforcement machine learning algorithms
These are just a few of the methods that enable the analysis of large quantities of data. They generally deliver fast and accurate results, however, they may require additional time and resources to train it properly. It is effective to combine machine learning with artificial intelligence and cognitive technologies.
For more information on machine learning:
- This catalog includes all of the popular machine learning algorithms, with brief descriptions of each.New!Like!
Machine Learning ChecklistThis checklist can be used to get good reliable results on machine learning problems.New!Like!
How to train a Machine Learning model in 5 minutes5 Step Process for training your own Machine Learning Model without the need for any coding knowledge or experience.New!Like!freeby Mate Labs
How to Define Your Machine Learning ProblemA catalog that contains 3 main steps that can help you define and solve your machine learning problems.New!Like!
How To Correctly Validate Machine Learning ModelsWhitepaper discussing the 4 main components for correctly validating machine learning models.New!Like!freeby RapidMiner
How to Prepare Data for Machine LearningAlgorithms learn form data, so it is vital to feed them the correct data. This will teach you how to prepare the data.New!Like!
How to Choose Machine Learning ModelA summary of each model's underlying algorithmic approach so you can sense whether it would be a good solution for you.New!Like!freeby Ricky Ho
How to Identify Outliers in your DataA guide that aids you to identify outliers in your data including certain methods that can detect outliers, too .New!Like!
How to Evaluate Machine Learning AlgorithmsA lot of time can be spent on choosing algorithms, this guide helps you quickly evaluate them.New!Like!
How to Choose the Right Test Options when Evaluating Machine Learning AlgorithmsThis tool explores the standard test options that can be used in algorithm evaluation and how to choose the right one.New!Like!
How to Improve Machine Learning ResultsThis tool provides methods that can improve the performance of machine learning algorithms.New!Like!
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