Machine Learning Algorithms

Machine Learning Algorithms

Linear Regression:

It is a category of supervised learning algorithms. This takes up independent variables and performs regression on them. It also means forecasting a task with dependent variables based on independent variables. There need not be only one type of regression model but it actually differs from variable to variable or the relationship between independent or dependent variables.

Linear regression is mostly calculated using scalar or matrix methods having a direct relationship between the variables which means if variable ‘a’ on the x-axis increases, variable ‘b’ will be increase. 

Support Vector Machine:

It is a type of machine learning algorithm which is not just used for regression and classification but outliers detection as well. It is used to plot points of raw data in an n-dimensional space which makes a hyperplane. There could be a number of hyperplanes between two types of data figures but the goal is to design the hyperplane which will have a maximum margin. This margin is required to create a boundary so that the learner can easily differentiate between the data points. A few advantages of support vector machines over other algorithms are it works in high dimensional space. It is very efficient and effective in memory management. This algorithm is perfect for identifying features such as if a person wants to plot the features of a cat and dog which have some similar and some different features. SVM will create a model which will have the extreme features of both cats and dogs and will create a margin between the two.

There is another term called kernel trick which is used in SVM learning meaning the similarity of data points will be calculated here making them separate linearly in a vast dimensional area. 

SVM is not fit for large datasets as a long duration of time is taken to perform classification on them using SVM. 

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Naive Bayes:

It is a category of supervised learning algorithms and is used to perform classification between various tasks. As we have discussed in SVM it takes up some similar features and some different features. In this category, there cannot be any same features. Hence, naive Bayes is used to classifying tasks that have no common relation amongst them. The performance is calculated using the Bayes theorem depending on conditional probability. For example, if a person wants to predict the weather forecast, it will plot the previous data of weather being windy, hot, humid, etc. the learner can then find out the prediction of the next day’s weather using the naive Bayes theorem.

(KNN) K-Nearest Neighbours:

It is a type of supervised machine learning algorithm which is used for regression and classification. The classification might be a very basic one yet it is the most important one. It is used the most for pattern identification as well as in data mining. This algorithm will store all possible and available points and will classify them as new cases by taking the most value voting of its k neighbors. It is not based on any assumption of data however the coordinates will be grouped based on the distribution data only.  The learner needs to fix an optimal value of k in this to get the prediction. The main key point one needs to not before using KNN is that it is slightly more expensive than other algorithms and the data has to be pre-processed before making use of it. It is very simple to use as it doesn't take up much data.

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K-Means Algorithm:

It is a type of unsupervised machine learning algorithm that helps the learner in solving clustering tasks and problems. Here, the data sets will be clustered into a number of clusters, say K. If the value of k=1. There will be 1 cluster, if the value of k=5, the number of clusters will be 5, and so on. This algorithm will help in clustering the data set without the requirement of any prior data All the data points are designed and form a big cluster out of the data from the other clusters. We also call these data points centroids. A cluster is formed from each centroid for each data point present. The process in k-means is repeated unless the user finds his best set of clusters.

Random Forest Algorithm:

This algorithm works as a collection of decision trees making a Random Forest. It will classify an object with some attributes and each tree will be classified based on the votes or its old features. The output will be predicted as the forest which will have the maximum number of votes as inputs. This algorithm tends to improve the accuracy of the dataset. As many trees as the user choose, more chances of better accuracy and fewer chances of overfitting problem. There is a set of assumptions that need to be followed to perform this such as the trees should avoid correlations amongst themselves. 
This algorithm is best suited as it takes up the least training data as compared to other learning algorithms. It predicts the output with great accuracy even where there is an issue of missing data.

Decision Tree:

This is one of the most popular learning algorithms in use today. It is a type of supervised machine learning algorithm and is mostly needed for classifying tasks and problems. It is best used for classifying both category-based and continuity-based data points. The user can use the above algorithm by splitting the population into 2 categories which are actually based on the independent variables. The algorithm is based on understanding of nodes, each depicting a particular attribute having a class. It will help in predicting the decision such as if the evening will be good for having a bonfire or not. On the basis of node attributes, the algorithm will help in predicting the output. However, there is not much need for computation to make a decision tree.

Hierarchical Clustering:

As the name says, it is a type of unsupervised learning which aims at making decision trees by making the use of separate data points and grouping them. The most important and good feature of this clustering is that the user doesn't always need to specify the number of clusters, hence they can be as many. The approach is followed as firstly each point of data is taken as a separate cluster and then based upon the similarity between them, similar kinds of data points form to make clusters together. The user can set up a threshold value to stop the clusters from being formed.

The difference between forming clusters with k-means and hierarchical clustering is that k-means don't form similar kinds of clusters however this algorithm forms the same clusters. Though, k-means learning is a slightly faster algorithm than this one.

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Machine Learning Categories 

Below are some machine learning categories which further have algorithms to make predictions for the tasks based on their learning. Lets have a look at them below:

Supervised Learning:

It is a type of machine learning which takes up trained data and a machine learning algorithm predicts output based on that data. It is a much simple method as compared to unsupervised learning and is highly accurate. Supervised learning includes problems based on classification as well as regression such as backpropagation, support vector machine etc.

Unsupervised Learning:

It is a type of machine learning which takes up untrained or unlabelled data and analysis it. These algorithms tend to work on hidden patterns in datasets. Unsupervised learning includes problems based on clustering such as the k-means algorithm etc.

Reinforcement Learning:

It is a category of machine learning which aims at understanding how intelligence works in an environment to meet the increasing demands. It is actually a practice where actions are performed and then evaluated on the basis of performance. In the case of positive feedback, a good action, good feedback is given however for a negative action, a bad feedback is given. It actually consists of problems that deal with sequential goals such as game playing.

Conclusion 

As this article gives a vast knowledge about what machine learning is along with the algorithms that are being followed under the same category. Be it any field such as medical, biology, chemical, robotics, banking, networks, DNA, etc. machine learning is playing a major role in making predictive outputs. Hence, the need of this era is to just dive into code and move ahead with this upcoming and increasing technology.