. Author: Or Machine Learning Source. Image by GDJ at Pixabay. A machine-learning algorithm is conceptually identical to all other computer science algorithms. A machine learning algorithm (or ML) is a process using data to build a ready-to-use model. Machine learning is like a train that propels you to accomplish a task. It is dependent on the specific business problem, complexity and available resources to determine the most efficient machine learning algorithm. There are several types of Machine Learning algorithms. Algorithms for Supervised Machine Learning Algorithms Algorithms which use machine learning to make predictions from a set of data. The supervised machine-learning algorithm searches for patterns within the value labels of data points. Unsupervised Machine Learning Algorithms The data points do not have any related labels. This machine learning algorithm clusters the data in order to understand its structure. It makes complex data easy and manageable for research. Semi-supervised Learning algorithms Semi-supervised is a type of machine learning that uses a combination of small amounts of labeled and large quantities of unlabeled data. Semi-supervised learning can be described as a combination of unsupervised (no labeled data for training) and supervised (only with labeled data for training). Machine Learning model Cheatsheet Top Machine Learning Methods Linear Regression Linear Regression illustrates the relationships between variables as well as the effects of changes in each variable. This algorithm shows the impact of changing the dependent variable. Because they give information about factors that affect the dependent variable, independent variables can be considered explanation variables. The dependent variable can also be called the predictor or interest factor. Linear regression can be used to calculate actual continuous values. Linear regression is used most often in the following areas: property price forecasting; weather forecasting; employee salary estimates. Linear regression’s fundamental goal is to determine the most appropriate line between predictions. The equation for linear regression is Y=a*x+b where y denotes the dependent variable, and x the collection of independent variables. The intercept and slope are denoted with a while b denotes the difference. For trend-based forecasting, linear regression is used often in sales forecasting. A linear regression analysis of monthly sales data can be used to determine future sales if a company is experiencing consistent sales growth month after month. Linear regression can be used to assess risk in financial or insurance domains. To determine how many claims each client has, a health insurance company may use linear regression to calculate their age. Insurance companies can use this research to determine if elderly clients are more likely to make insurance claims. This analysis is used to assess risk and make critical business decisions. 2. Logistic regression This algorithm employs a logistic function that predicts the outcome of categorical dependent variables using predictor variables. Explanatory factors are used to represent the probabilities and chances of an experiment’s outcome. Logistic regression algorithms help to estimate the probability of falling within a specific level of the dependent categorical variable based on the predictor factors. Example: The logistic regression algorithm is used in epidemiology to estimate risk factors and recommend preventative measures. These are useful in predicting the outcome of a particular election or determining whether someone will vote for one candidate. This is used to organize a group of words into nouns and pronouns as well as verbs and adjectives. It is also used in weather forecasting to predict the probability of rain. These are often used to predict an account’s default in credit rating systems. Sci-Kit Learn, a Python data science library implements Logistic Regression Machine Learning Algorithm. 3. Decision Tree A decision tree can be described as a graphic representation which uses branching to show all possible outcomes under a given circumstance. An attribute test is represented by the internal node in a decision tree. The test results are reflected in each branch. A specific class label is represented by the leaf node. To illustrate classification rules, the route from root to leaf node can be used. The most commonly used decision tree to categorize data is the classification trees. This depends on the response variable. They are used when the response variable is categorical. Regression Trees – When the target or response variable is numerical or consistent, regression trees can be used. They are used in many prediction problems, which is a different way than classification. Depending on the type of target variable, deci trees can be split into binary and continuous variables. To determine which type of decision tree is needed to address a particular issue, the target variable will be used. Example Decision trees can be used in option pricing finance. Remote sensing can be used to identify patterns using decision trees. Decision tree algorithms are used by banks to classify loan applicants according to their probability of defaulting. 4. Naive Bayes Classifier The Naive Bayes Classifier, which is based on similarities, is one of the most popular machine learning methods. This machine learning model is built on the well-known Bayes Theorem of Probability. It can be used to create machine learning models that predict disease and categorize documents. This is an exact categorization using the Bayes Probability Theorem to analyze subjective content. The Bayes Probability Theorem is used to compute probabilities in this classification algorithm. Naive Bayesian algorithms are built on the premise that each characteristic can be regarded independently of others. This is an easy method to use and is quite straightforward. This method is particularly useful for large datasets. It can also be used with text data. Facebook’s sentiment analysis is used to identify whether a status update expresses positive and negative emotions. Google uses document categorization to index documents. This is known as PageRank. PageRank is a classification system that considers pages which have been identified as important in databases. To categorize news articles on Technology, Entertainment, Sports, Politics, the Naive Bayes algorithm can also be used. Email Spam Filtering – Google Mail uses the Naive Bayes algorithm for determining if an email contains spam. 5. Artificial Neural Network Algorithm Artificial intelligence is based on brain-simulations. This is a type of network that includes neurons linked together. The linked structure can be used to make predictions about regression or classification problems. There are three layers to the ANN: one input layer and two hidden layers. The output layer is also included. There may be many hidden layers. Hidden layers are where calculations of the neural network take place. This layer contains the actual weights, biases equations, and application of activation function. This is a critical part of structuring and trimming output. It is mainly used for solving nonlinear problems like handwriting recognition. Because they require complex mathematical computations, ANNs can be computationally costly. Machine learning is used by financial institutions.
Five Popular Machine Learning algorithms.
