The machine learning model 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. The purpose of 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 various types of a result like prescriptive, predictive and descriptive analytics. There is a distinct difference between the machine learning model and algorithm. In the sequence of our learning, we will try to cover the difference and finally, we will describe the best 20 models for MI and its application.

**Difference Between Machine Learning Model and Algorithm**

At the beginning of the study, it is better to destroy the confusion between ML Model 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 than one algorithm. We can represent the equation in this way:

Model = Training (an Algorithm + Data)

**The Best Machine Learning Model**

Machine learning models are utilized for plotting any 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**

Linear Model is the relationship between two quantities represented through an equation to show a constant rate of change. It is used to make predictions using a Linear Function. when each term is either a constant or the product of a parameter and a predictor variable is also constant then we consider it is linear. By adding all the result the linear equation is constructed.

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

Y = b o + b1X1 + b2X2 + … + bkXk

There are various types of Linear Model. 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. Basic Non-Linear Model**

The opposite of linear is called non-linear. When the equation does not meed any criteria then it is considered as Basic Non-Linear Model. The data of non-linear is so long that it is difficult to fit on a graph. The accuracy of non-linear is much more difficult 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. Compare 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 machine learning model to use regression and classification related problems. It is known as 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 then 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 large 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 a large number of algorithms in a hidden layer (Sometimes over 100). This machine learning 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 a decision like the human brain based on a pattern. This subset of ML is considered as a deep neural network or learning.

Deep learning combines various data from various sources like an online portal, search engine, social media, and cloud computing. Those data construct a single platform called big data. It is so vast that the normal human brain will tale a thousand of year to get the ultimate result. With the big data model, artificial neural networks work like a human brain which is known as artificial intelligence. It mimics like a human brain but works within a few seconds.

**6. Decision Trees Machine Learning Model**

In real life, a tree has a lot of analogy with real problems. The machine learning model can be compared with a tree-like model with various conditions. To influence the decision making this model plot 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 machine learning model. This model is used in various areas like identifying customer segments, ensuring the guarantee of bank loan, the probability of passing your kid in the examination and finding weather an email in spam or the inbox. Using this model you can also predict the house price based on area, whether monsoon will be normal next year or not, approximate copies of books will be sold next year, etc. There are two types of classification models which are Binomial and Multi-Class.

**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 then 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.

**9. Regression 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 will be sold next month, the price or your house bases on the locality.

**10. Neural Networks Machine Learning Model**

Neural networks are a popular machine learning model inspired by a biological neural network. It does not work based on task-specific rules. It is also known as Artificial neural network (ANN). ANN can be used in many sectors like 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

**11. K-means Clustering**

K-means clustering is popular 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 the person who is conducting the analysis. If you want to segment a marker for product differentiation then you can use this machine learning model.

**12. Adaptive Resonance Theory**

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

The interesting 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.

**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 with 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 various sectors like manufacturing, inventory management, delivery management, finance sector, and power systems.

**14. Q-learning**

Q-learning model is the subset of the Reinforcement machine learning 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 any policy. The meaning of the word “Q” represents the quality of output. The output is shown by the Q table.

**15. Bayesian Network Machine Learning 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.

**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, it uses for big data analysis and data science.

**17. Nearest Neighbor**

Nearest Neighbor is an important 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 simplest classification algorithm that requires low time calculation. Nearest Neighbor is popularly known as the lazy learners model.

**18. Cross Decomposition**

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

**19. Gaussian Processes**

For the machine learning toolbox, Gaussian Processes uses as a powerful model. It predicts the data by incorporating prior knowledge. It is used for robotics or time series analysis. This model works as regression analysis but it is not limited to the regression model because it is extended to classification and clustering tasks. It is also used for the probability distribution, Marginalization and, Conditioning.

**20. Naive Bayes Machine Learning Model**

The last model of today’s discussion is the Naive Bayes machine learning model. It is a technique for constructing classifiers. This supervised ML technique is an old one that has been used since 1960. You can use this model in the pharmaceutical industry. You can also use for classifying various models of ML.

**Final Thought**

There are several machine learning models to solve a real-world problem. All the models are not required for you. Since it is a vast area so you have to be particular and specific. 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 whatever you like.