Machine Learning can analyze millions of data sets and recognize patterns within minutes. While we know what Machine Learning is and what it does, there’s little that is known about the different Machine Learning models types. Algorithms and models form the basis for Machine Learning programs and applications that enterprises use for their technical projects. 

There are multiple types of Machine Learning models. Each model provides specific instructions to the program for executing a task. In general, there are three different techniques under which different models can be classified. 

This article will highlight the different techniques used in Machine Learning development. After that, we will focus on the top Machine Learning models examples and algorithms that enable the execution of applications for deriving insights from data. 

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Machine Learning Development Techniques

There are three different types of techniques used in Machine Learning. They are based on the kind of input or output you want to generate and how you want the Machine Learning programs to work. 

  1. Supervised learning
    The supervised learning model takes a set of data as input and predicts a reasonable output from that data. It makes use of historical data to make the predictions. When there’s uncertainty, a supervised learning method is used to generate a known input and output. The data is labelled, and the model is trained to deliver a specific outcome every time some input data is fed to it.
  2. Unsupervised learning
    When there is labelled data, unsupervised learning is used for Machine Learning models. It is used for finding out inferences from unstructured data sets. There is no specific outcome that is mentioned in the beginning. The Machine Learning algorithms work on their own to identify similarities in data and automatically find a structure. Basically, it is used to find the hidden patterns in the data that make sense together.
  3. Reinforcement learning
    In the reinforcement technique of Machine Learning models types, the algorithms achieve a specific outcome after which there is some reinforcement for the same. The Machine Learning model takes action that goes toward the goal, after which it receives a reward. The reward for algorithms is the improvement in execution. The more times it achieves a specific outcome, the better it gets at performing the action.

Let’s have a look at the different models and Machine Learning algorithms that come under these three separate techniques of Machine Learning.

Read more: Python AI: Why Python is Better for Machine Learning and AI

Machine Learning Algorithms and Models for 2022

The types of Machine Learning models are mostly based on supervised learning as of now. Because there is so much uncertainty associated with unsupervised and reinforcement learning, most applications use supervised Machine Learning programs. Here is the Machine Learning models list that is primarily used by companies:- 

  • Linear regression
    One of the most popular Machine Learning models Python developers use is Linear Regression. The algorithm establishes a linear relationship between two variables to deliver a specific result. It is used for predictive analytics, forecasting supply & demand, and assessing the different trends in the market. There is a dependent variable and an independent variable, which is an explanatory one that determines the result.
  • Binary classification
    Most Machine Learning services companies build binary classification models using two variables. It offers either of the two possible outcomes. The binary classification Machine Learning model uses the logistic regression algorithm. There can be either “Yes or No”, “Right or Wrong”, or any other binary outcome for each execution. For example – “Is this email Spam?”. The answer could be Yes or No.
  • K-Nearest neighbours
    Mostly used for classification, K-nearest neighbour is one of the classics in the Machine Learning models list. It makes use of old available data to classify data into different categories. The Machine learning program will use the data to identify similarities in each case. The K-nearest neighbour algorithm involves a parameter that determines how many neighbours will be used in the classification system.
  • Decision trees
    Also known as regression trees, decision trees are extensively used for Machine Learning development. There is a tree-like model that learns from simple rules from the prior data in the tree. It makes observations about the input data and reaches a decision based on the branches and roots involved in reaching the conclusion. The route you take are the branches while each decision is called a node. They are used to predict survival rates, price of financial instruments, insurance premiums, and more. Checkout blog on Decision Tree Algorithm in Machine Learning
  • Naive Bayes
    Used primarily in text classification problems, Naive Bayes Machine Learning algorithms focus on working with high-dimensional data sets, such as filtering spam or classifying a huge amount of news articles. Each variable is regarded as independent, and the features of input data are completely unrelated. At first, it may seem complex, but it’s an extremely efficient probabilistic classifier for Machine Learning programs.
  • Multi-class classification
    Used by Python developers, the multi-class classification model is used to generate predictions and analysis for two or more classes. There are multiple data sets that are used to identify which dataset the outcome belongs to. The multi-class model predicts more than two outcomes from the input data. For example – “Is this product made of plastic, steel, or iron?” The model can classify the data and deliver from two or more outcomes.

Checkout blog on Machine Learning Solutions for Digital Transformation

Machine Learning Models are evolving in 2022

With each year, Machine Learning models are evolving. There are newer advancements that can enable better execution. Apart from the ones mentioned above, there is Random Forest, Logistic Regression, Linear Discriminant model, AdaBoost, and Vector Quantization, among several others that are used for Machine Learning development. 

Deciding which Machine Learning algorithms and models to choose depends on the type of data sets you have. Structured and unstructured data call for different model implementation. If you need proper consulting on Machine Learning development, our experts at BoTree Technologies can assist you. We provide complete Machine Learning services with different model implementations for predictive analytics, forecasting, pattern & image recognition, and more. 

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