Top 10 Machine Learning Algorithms

mediumThis post was originally published by Neelam Tyagi at Medium [AI]

This blog summarizes the most demanding and top machine learning algorithms that every data scientist and machine learning enthusiast must know.

It is undeniably, machine learning and artificial intelligence have become immensely notorious over the past few years. Also, at the moment, big data is gaining notoriety in the tech industry where machine learning is amazingly powerful for delivering predictions or forecasting recommendations, relied on the huge amount of data.

“Machine Learning is using data to answer questions” — Yufeng Guo

This article deals with the top machine learning algorithms that specify how and where such algorithms can be deployed along with a briefing note on what ML algorithms are and how they work.

What is Machine Learning Algorithms and How do They work?

Machine Learning Algorithms chart; Image: Source

Machine learning algorithms work on the concept of three ubiquitous learning models: supervised learning, unsupervised learning, and reinforcement learning.

  • Supervised learning is deployed in cases where a label data is available for specific datasets and identifies patterns within values labels assigned to data points.
  • Unsupervised learning is implemented in cases where the difficulty is to determine implicit connections in a given unlabeled dataset. (more want to learn about such learning models, click here)
  • Reinforcement learning selects an action, relied on each data point and after that learn how good the action was. (Related blog: Fundamentals to Reinforcement Learning- its Characteristics, Elements, and Applications)

Top Machine Learning Algorithm

Decision Tree

Naive Bayes Classifier

Ordinary Least Square Regression

Linear Regression

Logistic Regression

Top 10 Machine Learning Algorithms

Support Vector Machines

However, the optimal hyperplane, that can depart the two classes, is the line that holds the largest margin. Only such points are applicable in determining the hyperplane and the construction of the classifier and are termed as the support vectors as they support or define the hyperplane.

Clustering Algorithms

Gradient Boosting & AdaBoost

Principal Component Analysis

Deep Learning Algorithms

Conclusion

Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal. — Eric Schmidt (Google Chairman)

Not only these examples, but there are also various examples/applications that leverage ML potential at its entirety; some of them are as follows;

  1. 5 ways ML helps in Uber Services Optimization,
  2. How Spotify Uses Machine Learning Models?
  3. 7 Popular Applications of Machine Learning in Daily Life
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This post was originally published by Neelam Tyagi at Medium [AI]

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