Machine learning, artificial intelligence, and deep learning are technological innovations that are proving their worth in every industry that you can think of. No wonder they are the most discussed and popular terms across worldwide. Many people still think that these are concepts appeared out of the blue from nowhere; however, this is far from the truth.
These technologies have been around for several years, but it wasn’t until a few years back that they got the attention they deserve. The credit for making these innovations reach millions of people worldwide should go the people within the technological landscape.
Scientists, researchers, marketers and entrepreneurs have worked tirelessly to make others understand how these innovations have the capabilities of transforming the way we do business or approach and solve problems in our daily lives. The one thing that you need to understand is that though these technologies are related to each other, they are not same. This blog post will focus on machine learning and machine learning models.
How is machine learning making an impact?
In the past few years, machine learning has come into its own. People across the world have found out that machine learning has the power of making a difference. ML can completely change the way how people look at critical applications like image recognition, data mining, expert systems, natural language processing, and others. ML can offer solutions in all these areas and has the making of innovation that the humankind will be depending a lot on the times to come.
The popularity of machine learning and the understanding that it has what it takes to change things have resulted in a rise of demand for people who know how this technology works and how it can be used to solve real-life problems. Now let’s jump right into the topic.
Type of machine learning algorithms
Machine learning algorithms are well-defined programs that learn from data and improve over time. They don’t require human intervention to do their job. The tasks that these algorithms are supposed to learn may vary from instance-based learning and learning the mapping function that matches the input to the output to learning the unknown structure in an un-labelled data set, and more.
You need to understand that machine algorithms are the basis of the work that this technology is supposed to do. In other words, they make it tick. So, it becomes all the more that you choose the right ML algorithm for your needs. It is where a basic understanding of the concept is useful. You need to find an algorithm that fits the problem that you are seeking a solution for.
Also, machine learning algorithms help businesses to make decisions backed by data. This way, the chances that their decisions will pay dividends over time are very high. Now let’s turn our attention to the types of machine learning algorithms available to choose from.
Machine learning algorithms majorly fall under three basic categories – supervised learning, unsupervised learning, reinforcement learning. Supervised learning has a feedback feature that points out whether the prediction is right or wrong. There are some types of Supervised Learning, every data set has the desired output. The supervision takes place when a prediction produces an error to change the function and learn to map the input to the output.
Unsupervised learning doesn’t have anything to do with the response; it just uses its hidden structure to categorise data. You don’t have the desired output for a dataset in this machine-learning algorithm type. What happens instead is that the function tries to separate the data into different classes. This division is done in such a way that every separated class has a part of the data with common features.
Finally, reinforcement learning is in some way similar to supervised learning as it also gets feedback; however, this feedback is not received for every state or input. This ML algorithm type is focussed on learning actions for a few states that can help it to move to the desired state.
Unlike supervised learning in which error occurs after every example, reinforcement learning only records errors when a reinforcement signal is received. This behaviour has a lot of similarities with human learning, where you receive feedback only when a reward is imminent.
Machine learning algorithms have grown a lot over the years, and they are still evolving, matching the problems they are being used to find answers for. We currently have these three types that cover almost all machine learning models that are used today. In the future, we may have a few more types added to these three.
Most popular machine learning models
In this section, we will talk about machine learning models that are most commonly used. Let’s begin.
1. Linear regression
What is Linear Regression?So here it is,this algorithm makes predictions on the output variable based on one or more inputs variables. It is represented as a line – y=bx+c. Linear regression can be used to predict several things. You can use this model to predict the value of a house on the basis of its different attributes or properties, such as the number of rooms, total area, schools nearby, availability of transport, etc. It can also be used the predict the sale price of products for different parameters, such as customer behaviour.
2. Principal component analysis or PCA
It is referred to as a dimension-reduction model that is used to bring the variables present in a data set down to a minimum. It does this by putting together those variables whose scale of measurement is the same, and that have higher correlations than others. The purpose of this model is to filter the data set in such a way that we have access to new groups of variables that are still enough to describe its variability.
PCA is used in interpreting surveys that contain a lot of attributes or questions. For instance, surveys conducted to study culture, well-being, or behaviour usually have a lot of questions. With PCA, these questions can be grouped into principal components that can be easily explained in the survey report.
3. K-means clustering
This is a model that uses centroids or geometric centres as a reference to their observation clusters. The number of clusters used is decided by the person performing this analysis. It is often to analyse market segmentation – either to find out a similarity in customers or to discover a completely new customer segment.
4. Classification and regression trees (CART)
Decision trees are a very effective way to divide different findings and then put into groups. CART is a preferred and useful type of a decision tree that is used both for regression and classification. A response variable is selected, and the predictor variables are divided into groups. The number of divisions required is typically chosen by the machine itself to avoid instances of overfitting and underfitting. CART is effective where other models like black-box ones are usually not found fit due to the lack of clarity or transparency they provide.
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5. K-nearest neighbours or k-NN
This model can be used for either prediction or classification according to the variables in question. The model compares the closeness between observations that already exist in a data set and the ones that are newly formed. The machine does the math itself and selects the number of neighbours that need to be compared (k). It limits the occurrence of data underfitting and overfitting.
For classification, the closeness of most of the neighbours belonging to a specific class to the new observation determines the class of the new observation. In a prediction scenario, the value of the new observation is predicted by taking an average of the neighbours’ attributes that are being targeted. Learn more about KNN in R.
Machine learning is a branch based on artificial intelligence where data is used to identify patterns that can help make decisions without minimal human intervention. It is important to develop a deeper understanding of the models discussed here to start using them in real life.
There are many nuances in Machine Learning and its algorithms, such as linear aggression, logistic regression, Naïve Bayes, K-Means, that you will only come to know when you take this brief learning further ahead. ML is indeed a powerful tool that in the future will be used to find solutions to some of the most pressing problems of this world. Make sure you are always attuned to what’s happening around!
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What is the meaning of a Machine Learning Algorithm?
An algorithm means a method or procedure that will result in an output. The mechanism by which various technological systems like Artificial Intelligence perform their objective is known as a Machine Learning algorithm. In general, these algorithms are related to predicting output values from given input data. Classification and regression are the two basic phases of Machine Learning algorithms. Algorithms for Machine Learning are classified as either supervised or unsupervised. Unsupervised algorithms deal with data that is neither closed nor labeled, whereas supervised learning algorithms have both input data and intended output data supplied for them through labeling.
Which is the most widely used Machine Learning algorithm?
Linear Regression is one of the most widely used Machine Learning algorithms, which is used to estimate real values by making use of continuous variables. There are two forms of linear regression: simple linear regression and multiple linear regression. One independent variable characterizes simple linear regression. And, as the name implies, Several Linear Regression is characterized by multiple (more than one) independent variables. You can use a polynomial or curvilinear regression to get the best fit line. Polynomial or curvilinear regression is the term for this type of analysis. We can establish a link between the independent and dependent variables by selecting the optimal line.
What are the real-life use cases of Machine Learning algorithms?
Machine Learning algorithms help in using Machine Learning in our day-to-day life. One of the most common and widely used applications of Machine Learning is image and speech recognition. Image searches, face recognition, speech-to-text application, voice searches, etc., use Machine Learning algorithms. Machine Learning techniques can aid disease diagnosis. Many doctors use speech recognition chatbots to study the change in their patients' ailments. A trading Machine Learning algorithm is used to analyze a collection of stocks using economic characteristics and correlations in an arbitrage strategy (a finance strategy). Machine Learning algorithms can divide accessible data into categories, which are then defined by analyst-specified criteria.