Are you fascinated by machines? You might be even more fascinated by how they replicate and teach themselves human functions!
Machine learning is the new world’s job and has seen drastic growth in the last few years. In the coming time, it will only increase and help in raising our standards of living. Machine learning allows us to achieve a machine’s efficiency with a human capacity for sensitive problem-solving. Using various algorithms and codes, we can teach machines to emulate and understand specific tasks and then perform them themselves. The plus side is that they are much more efficient at it than humans. You can understand it as the ability exhibited by the machines to learn and improve on their own without explicitly re-programming them.
Thanks to ML algorithms, you can solve a single problem in multiple ways. An algorithm is a set of code or instructions that can perform a particular task. So, you can have an algorithm for calculating simple interest or one for translating languages.
These algorithms are studied multiple times and revised with each discovery. The modification and studies have tossed up several innovative algorithms to help one closely learn and know machines. One can learn about these algorithms and the machinery world with one click.
In this blog, we will talk about various machine learning algorithms and how they work. Also, what are the relevance of these algorithms in solving a problem? You might think it is hard to understand and deal with machinery knowledge, but our blog will help you get closer to understanding machine learning.
Top 10 Machine Learning Algorithms
Several techniques used to solve complex tasks break the problem into smaller segments. It helps in solving the problem efficiently in a given time. Besides, machine learning has come up as a handy tool to develop and achieve technical comforts.
The machine learning algorithm can term the best possible way to find a path from the input variable to the out variable using a functional value. Let us look at them in detail.
1. Naïve Bayes Classifier Algorithm
The task of arranging or sorting data text, webpages or, email manually sounds tedious. All of these tasks are time-consuming and need a lot of patience. However, this algorithm can make such tasks much easier. It makes use of a simple school-level probability class, Bayes Theorem, to predict future values. It allocates the element value to a population from a category of customised groups that are available. The formula mentioned below helps in evaluating the value of a prediction calculated in real-time.
P(y|X)= (P(X|y)P(y))/P(X)
where y is class variable and X is a dependent feature vector (of size n) where: X=(x1,x2,…..,xn).
An example of this is mail spamming filters, subscriber grouping algorithms, etc.
2. K means Clustering Algorithm
Searching the content on various search engines can be frustrating while working with the words when spelling the same but differ in the meaning. Homonyms can be confusing as you want to search for a particular semantic but result in the other one. You can achieve this by using this algorithm.
K Means Clustering uses K clusters for partitioning the results of the search into several formats. It, in general uses clusters, to evaluate the given set of data. This algorithm specialises in handling the K clusters and computes the result according to the input data set.
3. Support Vector Machine Algorithm
Flowcharts are diagrams that are easy to understand and calculate data. This algorithm classifies the given set of data into multiple field classes by searching a particular line, called a hyperplane. The hyperplane helps in dividing the information into several groups.
The support vector machine algorithm searches the particular hyperplane that maximises the distance between two classes. This hyperplane is known as the margin hyperplane, which helps in classifying the data set more accurately. An example of this algorithm is stock management, as it helps to take decisions in the financial field.
4. Apriori Algorithm
It is best to know some associations to evaluate the value of a particular thing. For example, if a person buys a vehicle then, they will also purchase insurance for it. The Apriori Algorithm helps to evaluate the various associations by studying the relations based on prior information. The probability analysis of the previous data helps in forecasting future assets and their needs.
An example of this algorithm is Google-automated tying and completes the phrase based on previously typed data.
5. Linear Regression Algorithm
The logistic regression algorithm is a technique derived from the field of statistics. Unlike linear regression, it uses a nonlinear function, called Logistic function, to compute the output.
This algorithm helps in understanding the relationship between the two variables, independent variable and dependent variable. It helps to understand the dominance of an independent variable over a dependent variable. It demonstrates the impact on the dependent variable when the value of the independent variable is changed.
The independent variable is termed as the explanatory variable, whereas the dependent variable is the factor of interest. An example of this algorithm is risk assessment in the insurance domain.
6. Logistic Regression Algorithm
This algorithm deals with discrete values. Discrete values are the types of value randomly given without any kind of relation, and cannot get grouped meaningfully. It helps in classifying the data into binary sets based on prior assumptions. If an event occurs, it terms it as one(1) else zero(0). Therefore, it predicts the occurrence of an event based on the given predictor variable. It is applicable in politics to predict the assurance of the candidate’s win.
Also Read : Linear Regression Vs. Logistic Regression
7. Decision Trees Algorithm
Consider you have to host a party on behalf of your parents. You have to decide on caterers, venues, dinner menus, etc., based on various evaluating factors like money, transportation, time, etc. Each of these problems can dissolve using this algorithm.
Decision Tree uses the yes/no format to evaluate a particular decision by considering the occurrence value of that situation. It uses branching methodology to branch out various outcomes and leads the result based on the information entered.
The output of the decision tree lies at the leaf node of the tree. The data divide into several parts until it reaches the leaf node where the result prediction takes place. Banks and financial institutions use this algorithm to evaluate the loan seekers, and their repayments of the debt extended.
8. Random Forests Algorithm
Random forest algorithm is the most robust algorithm among all and is popularly used to deal with various aspects of machine learning. It is typically termed bootstrap and uses statistical computations to make estimations on a given set of data.
Besides, Decision Trees can fall out when the number of deciding factors increases. This flaw is rectified here in this algorithm. The algorithm builds several decision trees based on the probability of occurrence. Every decision tree chart to a single tree termed CART (Classification and Regression Trees). Finally, the result estimates by pooling the results of all the decision trees. This algorithm is applicable in automobile companies to evaluate the breakdown of an automobile part in the future.
9. K Nearest Neighbour Algorithm
The K Nearest Neighbour Algorithm divides the given data set into several classes based on a particular function like distance. Then the prediction is made for the new data point searched among the K most similar nodes (neighbour), and the output of these K instances is summarised.
The K Nearest Neighbors Algorithm uses a lot of storage to store all the information but only performs calculations at the time of prediction. The simple way of using this algorithm is to find Euclidean distance, just by calculating the difference between each value in the given set.
10. Artificial Neural Networks Algorithm
Humans have reflexes that help us to decide if a situation changes. The human brain contains neurons to work in such circumstances. Similarly, Artificial Neural Networks Algorithm works on replicating these neurons by creating interconnected nodes. These nodes take information from one node, process it, and then pass the output to another node.
An example of this can be facial identification software such as Snapchat that uses AI to recognise facial structures and then applies filters accordingly.
Conclusion
For understanding and evaluating which path works the best for you, you need to study, understand and accumulate skills that work in your favour. The best way to gain ML knowledge is to enrol in an ML course that will teach you the nitty-gritty of machine learning concepts and algorithms. Once you’ve gained the fundamental knowledge, you can design unique algorithms or modify open-source codes found on the Internet.
upGrad gives you the platform to work closely with the algorithms and understand each one of them. You can attend various courses such as Executive PG Programme in Machine Learning & AI, Master of Science in Machine Learning and AI and, Master of Science in Machine Learning and AI to satiate your interest in technology and to develop the right employment skills.
