What is Machine Learning and Machine Learning Algorithms

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One of the massive buzzwords in recent times is machine learning. It is a term often coupled with AI (artificial intelligence). Machine Learning is always present in conferences, events, and talks centered on technological advances. Obviously, this term easily attracts the user’s attention through its appealing uses and applications.

In many cases, the term machine learning is often interchanged with artificial intelligence incorrectly. However, machine learning is the subfield of artificial intelligence.

It is also called predictive modeling and predictive analytics. American computer scientist Arthur Samuel, in 1959 first devised the term machine learning, which he explained, is the aptitude of the computer to learn without being unequivocally programmed.

Basically, machine-learning accesses programmed algorithms, which receive and analyze the input data in order to predict output values within the acceptable range.

As all the new data is fed to those algorithms, they are able to lean and optimize their operations to enhance performance and develop intelligence over time. Continue reading to know the importance of machine learning and types of machine learning algorithms!

What is machine learning?

Machine learning is an advanced concept, which lets the machine learn from experience and examples without being programmed in a detailed manner. Therefore, instead of writing the code for several pages, simply feed data to the generic algorithm and the machine develops the logic according to the given data.

Machine learning actually enables computers to tackle tasks carried out by people. Right from speech translation to driving advanced cars, machine learning is making a world lot of differences and evolution in the potential that AI holds.

It is being helpful for the software to derive meaning from the unpredictable and messy real world. In simple words, machine learning is the process of teaching the system how to make reliable and accurate predictions when the data is feed.

Probably, those predictions could be answering whether the use of word price in the hotel reservation, spotting an individual crossing the road in front of the self-driving car, finding the email is spam, or identifying the piece of fruit in the photo as banana or apple.

One of the key differences of machine learning from traditional computer software is that the human developers have not written code, which instructs the machine how to tell the difference between apple and grapes.

Rather than, the machine learning model widely depends on the huge amount of taught data and thus knows how to identify one fruit from another.

Tons of data is one of the key aspects for making machine learning possible. Look at the real-time examples of machine learning to get more clear and accurate understanding.

Real-time Examples of machine Learning

Have you ever shopped for products online? Well, did you notice that how the online site recommends the similar product you searching for when checking for a product? Did you notice how people buy the combination of products at affordable rates? All these things are because of machine learning.

Another real-time example of machine learning is insurance policies and loans. Do you ever have a call from the finance company or bank to get a loan or insurance policy? Have you ever thought about how they call you? Do they call everyone? No, they usually call a few chosen customers who they think buy their product.

Now, you will get a question in mind that is how they select individuals. This is the target marketing and can apply with the help of clustering, which is machine learning.

Likewise, plenty of day-to-day things are involved with machine learning, which people are unaware of it. Soon, the world of the machine becomes ruled by machine learning.

How does machine learning work?

Using the training data set, the machine-learning algorithm is well trained to create the model. Whenever new input data is given to a machine learning algorithm, it delivers the prediction based on the model.

The prediction is probably evaluated for accuracy. When it is acceptable, the machine learning algorithm will be deployed. On the other hand, the algorithm is trained repeatedly with the augmented training data set until the accuracy becomes acceptable.

Major reasons to use machine learning

Below mentioned are the most important reasons to utilize the machine learning concept.

Cheap and fast processing of data
Enhancing insight-driven decisions
Maximize competitive edge and return of investment
Better positioning of the business strategies
Obtaining real-time data for better marketing

Machine learning algorithms

When getting started, machine learning can be tough to understand. Plenty of algorithms and processes prescribed and utilized to make the machine learn how to predict the answer for a given input.

Knowing the algorithm appropriately helps you complete the implementation of machine learning. Additionally, it enables you to know the different methods available in the ground.

Availability of the algorithm in the machine-learning concept makes people often feel overwhelming. To lessen their burden, different types of machine learning algorithms divided into two groups such as learning style and similarity. Reading algorithms under this classification will aid you to have a better understanding.

Types of machine learning algorithms

Algorithms grouped by learning style

Depending upon the environment or the experience or any other form of input data, the algorithm can model an issue in different ways. It is extremely popular in machine learning and AI to consider learning styles firstly, which the algorithm can adapt. Three types of learning styles are available in the machine learning algorithms and they are:

Supervised learning

The computer is generally presented with certain example inputs according to which the desired outputs are to be achieved. In fact, the system was made to learn the general rules of converting the inputs to outputs.

The input data is known training data, which has a known result or label like a stock price.

The model is prepared via a training process in which it is needed to make predictions and then corrected whenever predictions go wrong. Until it reaches the specified accuracy level on the training data, the training process will be continued.

Regression and classification are the perfect examples of problems. The example algorithms are the backpropagation neural network and logistic regression.

Unsupervised learning

Since no labels are given to learning algorithms, it is quite hard to determine its own structure to deliver the output. This learning involves finding hidden patterns in data on its own.

It actually studies data to find the patterns. This means it does not have a label and known result as well.

The model is developed by inferring various available structures in the input data, used, probably to extract the basic rules, by the means of various mathematical procedures in order to minimize redundancy or organize data by similarity.

When it evaluates more data, its ability to make a decision on the specific data improves gradually and becomes more refined. Association rule learning, clustering, and dimensionality reduction are examples of problems. K-means and Apriori algorithms are the example algorithms.

Semi-supervised learning

It is quite similar to supervised learning but accesses both labeled and unlabeled data. The labeled data is information, which has meaningful tags, and therefore the algorithm can understand the data easily whereas unlabelled data lacks that information. This combination makes the machine-learning algorithm learn to label the unlabelled data.

Regression and classification are the example problems. The example algorithms are extensions to other flexible methods, which can make assumptions and predictions about how to model the unlabeled data easily.

Algorithms grouped by similarity

Often the similarity of the function is used as the basis to group Algorithms. For instance, Neural Network-inspired methods and tree-based methods. This is one of the most useful ways to group machine learning algorithms but it is not at all perfect.

Regression algorithms

Regression is generally concerned with the modeling relationship between variables, which is refined with the measure of the error in the predictions made by the model. This method is a mainstay of statistics and thus it is unified into statistical machine learning.

It majorly deals with the estimation of numerical and continuous variables. Using the regression, the estimations of stock price, housing price, and product price are determined. Popular regression algorithms are linear regression, step-wise regression, logistic regression, and so on.

Instance-based algorithms

This learning model is the decision problem along with the instances of training data, which are significant and needed to the model.

It develops the database of example data and then compares new data to the database with the help of similarity measures to find the perfect match and then make a prediction.

Because of this reason, it is called memory-based learning and winner-take-all methods. The focus is simply put on the stored instances representation and similarity measured accessed between instances. Self-organizing map locally weighted learning, and K-neared neighbor is famous instance-based algorithms.

Decision tree algorithms

This method builds the model of precisions made according to the actual values of the attributes in the data. Decisions keep on going in the tree structures until the proper prediction is made for the given data. Decision trees are probably trained on data for regression and classification issues.

This algorithm is often accurate and fastest as well as a big favorite in the concept of machine learning. Its popular algorithms are Conditional Decision Tress, Iterative Dichotomiser, M5, Decision Stump, and Classification & Regression Tree.

Bayesian algorithms

Bayesian methods are those, which straightly apply Bayes’s theorem for issues including regression and classification. The Naïve Bayes, Bayesian network, Gaussian Naïve Bayes, and Bayesian belief network are the most popular Bayesian algorithms.

Clustering algorithms

Just like regression, clustering describes the class of the issues and the class of methods. Typically, clustering methods are organized by appropriate modeling approaches such as hierarchal and centroid-based.

All these methods are concerned with the help of inherent structures in the data to organize the data perfectly into groups of maximum commonality. K-medians, k-Meas, and Hierarchical clustering are the popular clustering algorithms.

Deep learning algorithms

The method of deep learning is the modern update to the artificial neural networks, which utilize abundant cheap computation. They are highly concerned with developing more complex and much larger neural networks.

Most of the methods are attached to the semi-supervised learning issues where huge data sets comprise a low amount of labeled data. Its famous algorithms are Convolutional Neural Network, Deep Belief Networks, and Stacked Auto-encoders.

Artificial neural network algorithms

These models are extremely inspired by the structure and function of the biological neural networks. They are the perfect class of pattern matching, which are accessed commonly for classification and regression problems.

However, they are an enormous subfield containing hundreds of algorithms as well as variations for all kinds of problem types. The most accessible artificial neural network algorithms are Hopfield network, Perceptron, and Radial basis function network.

Other algorithms in machine learning

Below mentioned are algorithms, which are not covered in the above list.

Feature selection algorithms
Performance measures
Algorithm accuracy evaluation
Graphical models
Natural language processing
Computer vision
Computational intelligence and so on

How to choose the right machine language algorithm

Selecting the proper machine learning algorithm depends on many factors, which include diversity, data size, quality, and what kind of answers wish to derive from the given data.

Along with this, you will also require other considerations such as data points, training time, accuracy, parameters, and so on.

This is why choose the algorithm, which looks like a perfect combination of the business specification, requirements, time, and experimentation.

Even the most knowledgeable and experienced data scientists cannot able to tell you, which type of algorithm will work the best before experimenting with others.

Conclusion

Machine learning is definitely a breakthrough in the field of artificial intelligence because it enables the machines to learn from the real-time experience rather than programmed with each decision should make for every situation.

This concept can be applied to all kinds of applications such as object recognition, speech, Natural language processing, and predictive analytics. Machine learning will face a huge development in the upcoming years, which is potentially helpful for humans in many ways.