The machine learning model or MLM is a geometrical analysis and mathematically presentation of any work process or real-world problem. It requires a lot of data and algorithms to get the ultimate result. Creating the model is to provide extract insight from data to make better business and production decisions. By statistical and mathematical analysis, machine learning provides mixed prescriptive, predictive, and descriptive analytics results. There is a distinct difference between the MLM and algorithm. In our learning sequence, we will try to cover the difference, and finally, we will describe the best 20 models for MI and its application.

**Difference Between ML Model and Algorithm**

At the beginning of the study, it is better to destroy the confusion between MLM and algorithm. Because for beginners like me, it creates a lot of confusion. The model and algorithm are not the same.

When we train algorithms with data, it becomes a model. The learning model is a structure or equation, but the algorithm is some standard rules to make the model. To make a model, we need one or more algorithms. We can represent the equation in this way:

Model = Training (an Algorithm + Data)

**The Best Machine Learning Model**

Researchers use ML models to address and analyze real-world problems. It represents in various ways, like statistically, geometrically, and mathematically. Today’s we will discuss the best 20 ML models to solve real-world problems.

**1 . Linear Model**

A linear model represents the relationship between two quantities with an equation that shows a constant rate of change. It uses a linear function to make predictions. When each term is either a constant or the product of a parameter and a predictor variable, we consider it linear. By summing all the results, we construct the linear equation.

Result = constant + parameter * predictor + … + parameter * predictor

Y = b o + b1X1 + b2X2 + … + bkXk

Linear has various types of machine learning models. Such as:

- Ordinary Least Squares
- Ridge regression
- Lasso
- Multi-task Lasso
- Elastic-Net
- Multi-task Elastic-Net
- Least Angle Regression
- LARS Lasso
- Orthogonal Matching Pursuit (OMP)
- Bayesian Regression
- Logistic regression
- Stochastic Gradient Descent – SGD
- Perceptron
- Passive Aggressive Algorithms
- Robustness regression
- Polynomial regression

**2. Non-Linear Basic Model**

When the equation does not meet any criteria, then it is considered as Basic Non-Linear Model. The opposite of linear is called non-linear. The data non-linear is so long that it is difficult to fit on a graph. The accuracy of non-linear is much more complicated than it sounds. The observational data of the non-linear machine learning model is constructed by a formula where the model parameters are more independent. The model can be expressed by:

y ~ f(x,β)

**3. Ensemble Model**

Ensemble Model is used to combine multiple learning algorithms to get the perfective performance. It is one type of ML model that combines several models. Compared to a single model, it gives a better prediction. In the various machine learning competition, like the Netflix Competition, KDD 2009, and Kaggle the organizer use this model.

Because of bias ness, Noize and variance machine learning error may occur. Ensemble Model minimize those factors to improve accuracy and stability. It is used many ML hackathons to increase the ability of prediction. But, the disadvantage is it takes more time for computation and design time.

**4. Support Vector Machine Model**

Support Vector Machine is a supervised MLM machine learning to use regression and classification-related problems. It is known as a kernel trick to transform data and find an optimal boundary between the possible outputs. When the number of dimensions is greater than the number of samples, we use the Support Vector Machine Model. It works perfectly with a clear margin of separation in high-dimensional spaces. But, it is not suitable for extensive data set because it requires more time for training. On the other hand, when noize, bias ness, and variance are there, it does not work well.

**5. Deep Learning**

The word “Deep” refers to the employment of many algorithms in a hidden layer (Sometimes over 100). This ML model works on neural networks. Deep learning can be various types like supervised, semi-supervised and unsupervised. It is one type of artificial intelligence to make decisions like the human brain based on a pattern. This subset of ML is considered as a deep neural network or learning.

Deep learning combines data from various sources like an online portal, search engine, social media, and cloud computing. Those data construct a single platform called big data. You can compare it with the big data model, artificial neural networks work like a human brain known as artificial intelligence. It mimics a human brain but works within a few seconds. It is so vast that the average human brain will take a thousand years to get the ultimate result.

**6. Decision Trees Machine Learning Model**

In real life, a tree has a lot of analogies to real problems. The machine learning model can be compared with a tree-like model with various conditions. To influence the decision making this model plots different conditions visually. ML uses a powerful tool for prediction and classification. Because it can generate understandable rules for decision making, perform classification without requiring much computation and, handle both continuous and categorical variables.

The decision tree is easy to understand, interpret, visualize. It can handle numerical, categorical data, and multi-output problems. When it creates over-complex trees, it is known as overfitting. Where the value is continuous with various dimensions, the utilization of the decision tree is difficult. Implementation of it is expensive.

**7. Classification Model**

Classification is under the supervised ML model. This model is used in various areas like identifying customer segments, ensuring the guarantee of bank loans, the probability of passing your kid in the examination, and finding whether an email is spam or inbox. Using this model, you can also predict the house price based on area, whether monsoon will be expected next year or not, approximate books will be sold next year, etc. There are two types of classification models which are Binomial and Multi-Class.

A classification model in machine learning sorts data into distinct categories. For example, an email spam filter is a classification model that decides whether an email is spam or not. It learns from a dataset of labeled emails, some marked as spam and others as non-spam. By analyzing features like keywords and sender information, the model can then predict and classify new emails into either spam or non-spam categories.

**8. Multiclass Classification Model**

Multiclass Classification is a subset of classification with more than two classes. If confirms, a single sample should have only one label. For example, if we consider color, it should be red, blue, or ant single color. But, both can not be at the same time. When we use multiple labels to predict each instance, then it is considered as multi-label classification.

A multiclass classification model categorizes input data into one of several classes. For instance, an image classification model might be trained to recognize different types of fruits—like apples, oranges, and bananas. Given an image, the model analyzes its features and assigns it to one of the predefined categories. It learns from a dataset containing labeled images of these fruits, improving its accuracy over time to correctly classify new images into one of the fruit classes.

**9. Linear Regression Machine Learning Model**

When we target the numeric value, then we use the Regression Model. It is the relationship between one or more than one independent variable with a single dependent variable. Amazon is one of the best machine learning service providers in the world. They use this industry-standard model and called “linear regression”. You can solve the various problem using this Regression Model. For example, if you want to know the temperature of the next day, the number of products that will be sold next month, the price of your house based on the locality.

A regression model predicts a continuous value based on input data. For example, if you want to predict a person’s weight based on their height, you can use a regression model. You start by feeding the model data of people’s heights and weights. The model then learns the relationship between height and weight and uses this to predict weight for new heights. This way, you can estimate how much someone might weigh just by knowing their height.

**10. Neural Networks Machine Learning Model**

Inspired by biological neural networks, researchers popularized neural networks as a versatile ML model. They operate without relying on task-specific rules and are also known as artificial neural networks (ANNs). ANNs find applications across various sectors, including playing video games, speech recognition, machine translation, social network filtering, and medical diagnosis. There are many types of Neural networks architecture in machine learning which are:

- Perceptrons
- Convolutional Neural Networks
- Recurrent Neural Networks
- Long / Short Term Memory
- Gated Recurrent Unit
- Hopfield Network
- Boltzmann Machine
- Deep Belief Networks
- Autoencoders
- Generative Adversarial Network

A neural network is a machine learning model inspired by the human brain, designed to recognize patterns and make decisions. For example, a simple neural network for image recognition might consist of multiple layers of interconnected nodes, where each node processes a portion of the input data.

**11. K-means Clustering**

K-means clustering is famous for cluster analysis in data mining. It is a method of machine learning that creates a group of observations around the geometric centers. The meaning of the word “K” is the number of clusters determined by the person conducting the analysis. If you want to segment a product differentiation marker, you can use this MLM.

K-means clustering segments data into distinct groups based on feature similarities. It’s widely used for customer segmentation, image compression, and anomaly detection. For instance, in marketing, K-means can identify distinct customer groups for targeted campaigns. In image processing, it reduces colors for compression. The algorithm iteratively assigns data points to clusters and updates centroids to minimize variance, helping to uncover patterns and simplify complex datasets effectively.

**12. Adaptive Resonance Theory**

Adaptive resonance theory is a subset of the neural network ML model used for pattern recognition and prediction. It was first developed by Stephen Grossberg and Gail Carpenter in the year 1987.

The exciting nature of K-means clustering is always learning new input patterns without forgetting the old input pattern. It has various types like ART1, ART2, FuzzyART, ARTMAP and, FARTMAP.

An example of an Adaptive Resonance Theory (ART) model in machine learning is real-time pattern recognition in facial recognition systems. ART helps the system learn continuously as new faces are introduced without forgetting previously recognized ones. When the system encounters new images, the ART model either classifies them into existing categories or creates new ones.

Unlike traditional models, ART maintains stability while adapting to new data, thanks to its stability-plasticity mechanism. This enables the system to recognize both new and familiar faces effectively, making it ideal for dynamic environments that require ongoing learning.

**13. Reinforcement Learning**

Reinforcement learning is another popular machine learning model that is used for maximizing rewards in a particular situation. To take a specific action, it is employed to find the possible action and maximize the performance. It has a distinct difference from supervised learning based on taking data input. When there is no input, then it works from its experience. The reinforcement learning model is used in manufacturing, inventory management, delivery management, finance sector, and power systems.

An example of a reinforcement learning model is training an autonomous car to drive. The car learns to navigate roads by receiving feedback based on its actions. If the car stays in its lane, avoids obstacles, and follows traffic rules, it receives positive rewards. However, if it crashes or moves off course, it receives negative feedback.

Over time, the model learns the best driving strategies by adjusting its behavior to maximize rewards. Through trial and error, the car improves its driving performance, aiming to reach its destination safely and efficiently. This reinforcement learning approach allows the car to learn complex tasks in dynamic environments.

**14. Q-learning**

Q-learning model is the subset of the Reinforcement ML model where it tells its agent to take action based on various circumstances. Without any adaptation, this ML model can handle any problems. This off-learning reinforcement model provides the best action in the present scenario without any prescribed policy. The meaning of the word “Q” represents the quality of output. The output is shown by the Q table.

A classic example of the Q-learning machine learning model is training a robot to navigate a maze. In this scenario, the robot learns the optimal path by exploring the environment and receiving rewards or penalties. Each time it takes a step in the maze, the Q-learning algorithm updates a “Q-value” for that action.

If the step brings the robot closer to the goal, it receives a positive reward and the Q-value increases. If the action leads to a dead end or obstacle, the Q-value decreases. Over time, the robot learns the best actions to take at each point to maximize its rewards and reach the goal efficiently. Q-learning is a type of reinforcement learning that helps agents make decisions based on experience.

**15. Bayesian Network ML Model**

Bayesian Network is a traditional probabilistic technique that represents problems in a graphical model. It has two parts structure and parameters. It is a joint probability distribution that provides compact, flexible, and interpretable representation. Compact, flexible and interpretable representation. Bayesian Network has a random variable and graph structure to encodes the dependencies between the variables. The graphical presentation will clear the concept.

An example of a Bayesian Network machine learning model is diagnosing diseases in healthcare. Doctors input symptoms, patient history, and test results into the model. The Bayesian Network then calculates the probability of various diseases.

For instance, if a patient has a fever, cough, and sore throat, the model assesses the likelihood of conditions like the flu, cold, or pneumonia. It uses conditional probabilities to update its predictions as new information becomes available. This helps doctors make informed decisions by considering both the likelihood of diseases and the relationships between symptoms and conditions.

**16. Probabilistic Model**

The probabilistic model is the heart of machine learning. With the help of random variables, this powerful idiom describes the real world. It works based on incorporate random variables, normal distribution, binomial distribution, and Bernoulli distribution. The statistician uses this model in the life insurance calculation.

In machine learning, researchers use probabilistic models for extensive data analysis and data science. For example, in weather forecasting, a probabilistic model such as a Bayesian Network predicts the likelihood of various weather conditions (rain, sunshine, etc.) based on historical data.

The model takes into account multiple factors such as temperature, humidity, wind speed, and historical patterns. It estimates the probability of future weather events by updating its predictions as new data comes in. This helps in predicting the weather with varying degrees of certainty, giving percentages (e.g., 60% chance of rain) instead of just a binary forecast. Probabilistic models are ideal for handling uncertainty and making predictions in complex, data-rich environments.

**17. Nearest Neighbor**

Nearest Neighbor is an essential and powerful machine learning model that is easy to implement and solve regression and classification models. It is also used for solving the industrial problem. It is the most straightforward classification algorithm that requires low-time calculation. Nearest Neighbor is popularly known as the lazy learners model.

A common example of the Nearest Neighbor machine learning model is a recommendation system for online shopping. When a user searches for or buys a product, the model looks at similar customers’ purchase history. Using the K-Nearest Neighbors (KNN) algorithm, it identifies other users with similar shopping behaviors.

**18. Cross Decomposition**

Cross decomposition is a high-level ML model that uses two algorithm types: partial least squares (PLS) and canonical correlation analysis (CCA). This model linear relations between two multivariate datasets: the X and Y. The output shows on a scatterplot matrix display.

An example of cross decomposition in machine learning is predicting product sales based on advertising data. Cross-decomposition techniques, such as Partial Least Squares (PLS), find relationships between two datasets: one containing advertising data (e.g., TV, radio, and online ads) and the other containing product sales.

**19. Gaussian Processes Time Series ML**

For the machine learning toolbox, Gaussian Processes uses as a powerful model. It predicts the data by incorporating prior knowledge. Researchers use this model in robotics and time series analysis. While it operates similarly to regression analysis, it extends beyond regression to include classification and clustering tasks. Additionally, it handles probability distributions, marginalization, and conditioning.

A great example of using Gaussian Processes (GP) in time series machine learning is predicting stock prices. In this case, the model captures the underlying patterns in historical stock price data and uses them to predict future trends. Gaussian Processes are flexible and can provide uncertainty estimates for predictions, which is useful in volatile markets like stocks.

**20. Naive Bayes Machine Learning Model**

The last model of today’s discussion is the Naive Bayes ML model. It is a technique for constructing classifiers. This supervised machine learning technique, used since the 1960s, is a longstanding method. You can use this model in the pharmaceutical industry. You can also use it for classifying various models of ML.

An example of the Naive Bayes machine learning model is email spam detection. In this case, the model analyzes the words in an email and assigns probabilities to determine whether it is spam or not.

**Time Series Model Machine Learning**

Time series in machine learning involves analyzing data points collected over time. It helps predict future trends based on past patterns. The best machine learning model for time series data depends on the use case. Popular models include ARIMA, LSTM, and Prophet. Each model handles time-related data in unique ways. There are four main types of time series models. These include Autoregressive models (AR), Moving Average (MA), ARMA, and ARIMA. Autoregressive models use past data points to predict future ones. Moving Average models use past forecast errors. ARMA combines both AR and MA features. ARIMA adds the concept of differencing for non-stationary data.

**Final Thought**

There are several machine learning models to solve real-world problems. You do not need all the models. Since it is a vast area, so you have to be particular. On the other hand, the required machine learning language is also specific, like python, R, and java. The purpose of today’s discussion is to provide you the ML model idea in generic terms. In my subsequent article, I will describe topics based on mathematics. If you find it interesting, please share or comments on whatever you like.

Nasir H is a business consultant and researcher of Artificial Intelligence. He has completed his bachelor’s and master’s degree in Management Information Systems. Moreover, the writer is 15 years of experienced writer and content developer on different technology topics. He loves to read, write and teach critical technological applications in an easier way. Follow the writer to learn the new technology trends like AI, ML, DL, NPL, and BI.