Machine Learning algorithms are programs that can learn the hidden patterns from the data, predict the output, and improve the performance from experiences on their own. Different algorithms can be used in machine learning for different tasks, such as simple linear regression that can be Machine Learning algorithms are the programs that can learn the hidden patterns from the data, predict the output, and improve the performance from experiences on their own. Machine Learning algorithms are programs that can learn the hidden patterns from the data, predict the output, and improve the performance from experiences on their own. Different algorithms can be used in machine learning for different tasks, such as simple linear regression that can be used for prediction problems like stock market prediction,and the KNN algorithm can be used for classification problems.
It can be broadly classified into three
- supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Reinforcement Learning algorithm
Supervised learning is a type of Machine learning in which the machine needs external supervision to learn. The supervised learning models are trained using the labeled dataset. Once the training and processing are done, the model is tested by providing sample test data to check whether it predicts the correct output. The data will be labeled in supervised learning. The goal of supervised learning is to map input data with output data. An example of supervised learning is spam filtering.
Supervised learning can be divided further into two categories of problems :
- Classification
- Regression
Examples of some popular supervised learning algorithms are Simple Linear regression, Decision Tree, Logistic Regression, KNN algorithm, etc.
Unsupervised learning
It is a type of machine learning in which the machine does not need any external supervision to learn from the data, hence called unsupervised learning. The unsupervised models can be trained using the unlabelled dataset that is not classified, nor categorized, and the algorithm needs to act on that data without any supervision. In unsupervised learning, the model doesn’t have a predefined output, and it tries to find useful insights from a huge amount of data. These are used to solve the Association and Clustering problems. Hence further, it can be classified into two types
- Clustering
- Association
Examples of some Unsupervised learning algorithms are K-means Clustering, Apriori Algorithm, Eclat, etc
Reinforcement Learning
In Reinforcement learning, an agent interacts with its environment by producing actions and learning with the help of feedback. The feedback is given to the agent in the form of rewards, such as for each good action, he gets a positive reward, and for each bad action, he gets a negative reward. There is no supervision provided to the agent. The q-Learning algorithm is used in reinforcement learning.
List of some Popular Machine Learning Algorithms
- Linear Regression Algorithm
- Logistic Regression Algorithm
- Decision Tree
- SVM
- Naïve Bayes
- KNN
- K-Means Clustering
- Random Forest
- Apriori
- PCA