Machine Learning is an application of AI that focuses on the development of system programs that provides the ability to learn automatically. Machine Learning Models are the mathematical representation of the real world processes. These models are the outcome of the training process which can be used for making predictions. Moreover, this training process continues until the model achieves its desired level of accuracy on the training data.

There are a number of Machine Learning models available. The Machine Learning methods are depended on the type of tasks they deal with. Furthermore, they are classified into Classification models, Regression models, and Clustering, Principle Component Analysis & Dimensional reductions.

Types of Machine learning models

The different types of Machine learning models based on the types of tasks are as follows.

Classification Models

The Classification model tries to draw a conclusion by observing several values. A classification model will attempt to predict the value of one or more outcomes. Besides, the outcome for classification is always variable. Moreover, there are many types of Classification models. Such as decision tree, logistic regression, random forest, multilayer perception, Naïve Bayes, one-Vs-rest, etc. A few of these models discussed as follows.

Decision tree: A decision tree under classification models is a way to make a decision by spitting the inputs into small decision pieces. Moreover, it involves some simple mathematics too. The tree is divided into decision nodes and leaves.

Logistic regression: A logistic regression is a variation of linear regression. It attempts to calculate a dependent variable based on an independent variable.

Random forest: This classification model is like a decision tree model. Here the questions that come forward include some randomness.

Naive Bayes: The theory of Naïve Bayes is useful to detect or classify email whether spam or not. Bayes is a regular statistic where the concept is based on dependent probability. Moreover, the dependent probability is totally based on the chances of the outcome.

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Regression model

Under Machine learning models, the regression refers to a certain set of problems that the output variable can take continuous variables. Predicting airlines ticket comes under this model approach. There are some important regression models commonly in practice such as linear regression, non-linear regression, and probabilistic model.

Clustering

The clustering under Machine learning models is the task of grouping similar objects together. The Machine Learning models help to detect similar objects without the intervention of human beings. Without having homogenous data, it is a little difficult to build Machine learning models. Moreover, there are different types of clustering models such as K means, K means ++, DBSCAN, etc. The clustering is further classified into different models.

• Connectivity models
• Centroid models
• Density models
• Distribution models

Dimensionality reduction

Dimensionality refers to the number of prediction variables useful to predict the independent variable. While in the real-world data sets, there are a number of variables. This huge number of variables may overfit the model. Moreover out of these numbers of variables, most of the variables don’t contribute equally towards the goal. There are some most common models of dimensional reduction that are in use. These are,

PCA- This approach creates less number of variables out of the huge number of predictions.

TSNE- It provides lower dimensional embedding of high data points that generate in the process.

SVD- It refers to singular value decomposition that is useful to decompose the matrix into small parts for efficient calculations.

Moreover, in dimensional reduction, there are few more methods that are useful. Such as a) Feature selection methods, b) Feature projection methods. The feature selection methods include filter strategy, wrapper strategy & embedded strategy. Furthermore, the feature projection method includes linear methods, non-linear methods, and tensor representations.

Deep Learning

Deep learning is the subset of ML and it deals with neural networks. Moreover, there are different types of deep learning models base on neural networks. They are,

• Convolution Neural networks
• Multi-layer perception
• Boltzmann Machine
• Recurrent neural networks
• Autoencoders

Moreover, there are some artificial neural networks such as feedback ANN, feed-forward ANN, and long short term memory networks.

Machine learning models for prediction

Machine learning models can be used for making various predictions. These predictions are most valuable for any business model. Besides, there are many kinds of predictions that machine learning professionals make in this regard. For example, we can make predictions of the stock market regarding stock prices, Sensex ups, and downs, market conditions, etc. Moreover, there are two basic types of approaches in stock market analysis. They are fundamental analysis and technical analysis.

Fundamental analysis- A fundamental analysis includes analysis of the company’s future profitability and its asset size. It depends upon the company’s existing business environment and financial conditions. Moreover, the performance of business also an important thing here.

Technical analysis- The technical analysis includes some technical points. It involves reading the charts and graphs using statistical figures. Moreover, it identifies the changing trends in the stock market.

The machine learning models and approaches that apply in this regard are,

• Moving average
• Linear regression
• K-Nearest neighbors
• Auto ARIMA
• Prophet
• LSTM

Let’s discuss it in detail.

Moving Average

An average is the most common thing that we use in our day to day life such as doing some calculations. For example, we can calculate the average temperature of the past few days or the price of petrol/diesel that changes daily. Besides, this approach is very much useful for predicting market prices. Such as predicting the closing price of the market for every day close. Moreover, the usage of the moving average technique over a simple average is more beneficial to predict the latest set of values.

K-Nearest Neighbors

It is one of the most useful machine learning algorithms known as kNN. Moreover, this approach is based on the independent variables, where the kNN finds the similarity between the two data points. These are new and old data points. 

Auto ARIMA

ARIMA is a popular statistical method. It is useful for forecasting time series where these models use past values for predicting new values. Furthermore, the ARIMA model has three different parameters. These are;

• P (it refers to the past values that used to predict future values)
• Q (it refers to the past predicted errors that are useful to predict future values)
• D (this refers to the order of differencing)

Moreover, the professionals use the Auto ARIMA model for making various predictions as the ARIMA models consume a lot of time for tuning parameters.

LSTM

This refers to the long short term model under Machine learning models for prediction approach. This model is useful widely in making sequence predictions. Moreover, this model helps to store past information that is important and neglects the unimportant one. The LSTM model uses three gates in its approach. These are; the input gate, the forget gate, and the output gate.

Furthermore, the stock market prices are also affected by global happenings. It depends upon the trends but sometimes it is difficult to make predictions based on uncertain things.

Thus, the different types of Machine learning models approach to solve different issues. These models help in various ways. Machine learning is very useful in different fields. Using this many businesses can achieve their goals and makes their business better.

To get more knowledge in this regard and to get practically skilled with various Machine learning techniques and tools one can opt for Machine Learning Online Training from industry experts. This learning will help to enhance skills and paves the way for a better career.

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