Machine Learning (ML) is a subset of Artificial Intelligence (A.I.), driving the new-age business technologies and is transforming every sector. Machine Learning is the study of mathematical algorithms which impart machines the ability to self-learn through data and experience and improve over time to offer better results. You have been using ML for a long time now even without you realising. Yes, you are accessing mails, which are automatically classified as spam, forums, ads, etc. and the web searches you perform are increasingly becoming relevant and purpose driven is the result of Machine Learning in-action.
Here I will discuss various ML Algorithms that are leveraged across industries to drive growth and efficiency on a large scale. If you are excited about learning ML, have a look into this AI ML Courses, a Post Graduate Diploma by NIT Warangal. Before that, let’s discuss a bit more about the context of these algorithms.
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Predictive Analytics is extensively used by various organizations across the world in predicting future trends and events using historical data through the help of ML algorithms. Whereas, Descriptive and Diagnostic Analytics relies on what did happen? How did that happen? Whereas Predictive and Prescriptive Analytics focus on what is yet to happen.
- Cluster Model
- Classification Model
- Outliers Modell
Cluster Modelling segregates users on their shared behaviors and traits into groups and further subgroups or nested groups.
Classification Modelling is easy and answers a question of Yes/No as a reply. It learns from historical data and then classifies data to help you answer the question. Due to its simplicity, it exists across various industries.
Outliers Models are used where we need to find out the fraudulent entry automatically.
There are quite a few popular ML algorithms that are predicting future trends. And they are:
- Random Forest
- Gradient Boosted Machine(GBM)
- Generalized Linear Model (GLM)
Random Forest is the most famous classification ML algorithm that exists today. Its name is derived from natural forest, as it uses trees or decision trees to find the accurate output. The more the trees, the more will be the accuracy. It restricts overfitting and votes on the best solution from data samples from all the decision trees. Check out this Machine Learning Tutorial to get a better understanding of ML algorithms .
Gradient Boosted Machine (GBM) is used to build prediction models, using decision trees, just like in Random Forest. But here in GBM, one tree is built at a time and considers the accuracy and errors in the previously trained tree, unlike Random Forest.
Generalized Linear Model (GLM) brings every Regression model like the Classical Linear Model, models for data counts, and survival models all in a single platform. GLM’s are similar to Classical Linear Regression models.
The best Predictive Models and Machine Learning Algorithms depend on individual business use-cases and certain factors like accuracy, speed, agility, and reliability they require for proper functioning.