Outline of machine learning
The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a modelfrom an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
A Tour of Machine Learning Algorithms
In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available.
There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit.
I want to give you two ways to think about and categorize the algorithms you may come across in the field.
After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related.
More details: https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit.
I want to give you two ways to think about and categorize the algorithms you may come across in the field.
- The first is a grouping of algorithms by the learning style.
- The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together).
After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related.
More details: https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Top 10 Machine Learning Algorithms For Beginners
To give you an example of the impact of machine learning, Man group’s AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less.
After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. They are:
After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. They are:
Other Lists of Algorithms
There are other great lists of algorithms out there if you’re interested. Below are few hand selected examples.
- List of Machine Learning Algorithms: On Wikipedia. Although extensive, I do not find this list or the organization of the algorithms particularly useful.
- Machine Learning Algorithms Category: Also on Wikipedia, slightly more useful than Wikipedias great list above. It organizes algorithms alphabetically.
- CRAN Task View: Machine Learning & Statistical Learning: A list of all the packages and all the algorithms supported by each machine learning package in R. Gives you a grounded feeling of what’s out there and what people are using for analysis day-to-day.
- Top 10 Algorithms in Data Mining: Published article and now a book (Affiliate Link) on the most popular algorithms for data mining. Another grounded and less overwhelming take on methods that you could go off and learn deeply.
