Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Types of Machine Learning Algorithms
How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.
Under the umbrella of supervised learning fall: Classification, Regression and Forecasting.
- Classification: In classification tasks, the machine learning program must draw a conclusion from observed values and determine to
- what category new observations belong. For example, when filtering emails as ‘spam’ or ‘not spam’, the program must look at existing observational data and filter the emails accordingly.
- Regression: In regression tasks, the machine learning program must estimate – and understand – the relationships among variables. Regression analysis focuses on one dependent variable and a series of other changing variables – making it particularly useful for prediction and forecasting.
- Forecasting: Forecasting is the process of making predictions about the future based on the past and present data, and is commonly used to analyse trends.
Semi-supervised learning is similar to supervised learning, but instead uses both labelled and unlabelled data. Labelled data is essentially information that has meaningful tags so that the algorithm can understand the data, whilst unlabelled data lacks that information. By using this
combination, machine learning algorithms can learn to label unlabelled data.
Examples of Supervised Learning:
- Regression
- Decision Tree
- Random Forest
- KNN
- Logistic Regression etc.
How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.
Under the umbrella of unsupervised learning, fall:
- Clustering: Clustering involves grouping sets of similar data (based on defined criteria). It’s useful for segmenting data into several groups and performing analysis on each data set to find patterns.
- Dimension reduction: Dimension reduction reduces the number of variables being considered to find the exact information required.
Examples of Unsupervised Learning:
3. Reinforcement Learning
How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
Example of Reinforcement Learning:
Machine Learning Algorithms
Here is the list of commonly used machine learning algorithms.
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM
- Naive Bayes
- kNN
- K-Means
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
- GBM
- XGBoost
- LightGBM
- CatBoost
- https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
- https://skymind.ai/wiki/machine-learning-algorithms
- https://searchenterpriseai.techtarget.com/definition/machine-learning-ML
- https://www.expertsystem.com/machine-learning-definition/
- https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/
- https://www.sas.com/en_ie/insights/articles/analytics/machine-learning-algorithms.html
- https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machine-learning-newbies-dde4edffae11