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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:
- Senior Data Scientist Zach Millar explains how you can learn machine learning in 6 months through a roadmap process.26 remove_red_eyeLike!freeby IDEAS
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.23 remove_red_eyeLike!freeby Mate Labs
How To Correctly Validate Machine Learning ModelsWhitepaper discussing the 4 main components for correctly validating machine learning models.20 remove_red_eyeLike!freeby RapidMiner
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
Machine Learning Algorithms TutorialTeaching the basics of machine learning, along with the ways in which you can use machine learning for problem solving.13 remove_red_eye2
The Top 5 Algorithms used in Data ScienceThis video discusses the 5 most widely used algorithms in Data Science and how to use them.10 remove_red_eye2
Building Robust Machine Learning ModelsThis presentation focuses on the fundamentals of building robust machine learning models.New!Like!
Measuring Model PerformanceVideo tutorial on how to measure your model's performance.New!Like!freeby Data Camp
Choosing the Right Machine Learning AlgorithmSeth Mottaghinejad discusses the things we should be thinking about when choosing a machine learning algorithm.New!Like!
How to Predict Stock Prices Using Machine LearningSiraj Raval demonstrates how to build a stock prices prediction script in 40 lines of Python.New!Like!freeby Siraj Raval
- Have a Machine Learning Algorithm to share?
Your Machine Learning AlgorithmPublish your Algorithm
Any questions on Machine Learning?
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