Machine Learning Algorithms
ML algorithms are those that can learn from data and improve from experience, without human intervention.
According to our problem statement, we should choose the algorithm. Before choosing an algorithm for any problem statement one must be aware of different types of algorithm and their functions. For that let’s discuss the types of machine learning algorithms.
Classification:
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
Regression:
Regression is a ML algorithm that can be trained to predict real numbered outputs; like temperature, stock price, etc. Regression is based on a hypothesis that can be linear, quadratic, polynomial, non-linear, etc. The hypothesis is a function based on some hidden parameters and input values.
Clustering:
Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning and a common technique for statistical data analysis used in many fields.
Some frequently used Algorithms:
1.Supervised Learning
- Linear Regression
- Logistic Regression
- Polynomial regression
- Decision Tree
- Support Vector Machine (SVM)
- K-nearest Neighbors
- Na?ve Bayes
- Random Forest.
2.Unsupervised Learning
- K-Mean Clustering
- Hierarchical Clustering
- Hidden Markov Model
- Association Rules
3. Reinforcement Learning
- Q-learning
- Temporal Difference(TD)
- Deep Adversarial Networks