Machine Learning Algorithms | Python
Machine learning algorithms help you to answer the questions that are too complex to answer through manual analysis. The purpose of Machine Learning (ML) is to learn from the data. In a machine learning model, the goal is to learn from data and improve from experience, without much human intervention. Different Machine Learning (ML) algorithms analyse data in different ways. So, they can be divided into 3 broad categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In Supervised Learning algorithms, the input dataset is divided into train and test dataset. After that a model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. Majority of the machine learning algorithms fall under the supervised learning category, Regression and Classification.Regression algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Ordinary Least Squares Regression
- Polynomial Regression
- Logistic Regression
- K-Nearest Neighbor
- Decision Tree
- Random Forest
- Support Vector Machine
In Unsupervised Learning algorithms, you do not have any target or outcome variable to predict or estimate. The algorithms itself learns few features from the current dataset. When new dataset is introduced, it uses the previously learned features to realize the type of the data. Unsupervised Learning is mainly used for clustering and feature reduction.
Reinforcement Learning is about taking suitable action to maximize reward in a particular situation. It is a feedback-based technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. Unlike supervised learning , the agent learns automatically using feedbacks without any labelled data.
For machine learning beginners who are eager to learn the basic of this technique, here is a quick tour on the top Machine Learning algorithms used by data scientists.