## Popular Algorithms in Machine Learning (Data Sciences)

We are living in a world where almost all manual jobs are being done automatically. The algorithms of Machine Learning can enable computers perform surgeries, play chess, and even get more personal and smarter. We are going to see constant evolution in technology. By considering how computing has evolved over the years, it is easy to predict what would come next. Over the past couple of years, data scientists have made the unique data crunching machines with advanced technologies. Here are some of the common Machine Learning Algorithms to make lives of Data Scientists easier.
Linear Regression
It is probably one of the most popular and common algorithms used in machine learning and algorithms. Predictive modeling is usually based on reducing the error of model or making it possible to do most accurate predictions at the cost of explainability. We will reuse, borrow and steal algorithms from different fields like statistics and they can be used on these ends.
Logistic Regression
It is another algorithm for machine learning used in the field of statistics. It is a go-to technique for problems with two class values or binary classification problems. Logistic regression works like linear regression. The key here is to find values for coefficients which measure each variable. The prediction for output is transformed with logistic function, unlike linear regression.
Logistic regression works better by removing attributes which are not related to output variable along with attributes which are correlated to one another like linear regression. This model is fast to learn and very effective on binary classification covered in Python courses in Delhi.
Support Vector Machine (SVM)
It is actually a method of classification in which raw data is plotted as points in n-dimensional space (n is number of features). Then, each feature’s value is tied to a specific coordinate so it can be easier to classify data. Lines known as classifiers can be used to split data and plot the same on graph.
Linear Discriminant Analysis
The classification algorithm of Logistic Regression is actually limited to two-class classification. If there are several classes, it is better to choose Linear Discriminant Analysis, which is a linear classification algorithm. It is very easy to represent LDA. It has statistical aspects of your data which is calculated in each class. It includes the following for single input variable –
Variable in all classes
Each class’s mean value
You can predict by estimating discriminate value for all classes and predict the largest value for the class. In this technique, it is assumed that data gas bell curve of Gaussian distribution. So, it is wise to get rid of outliers from the data beforehand. It is a very powerful and easy process for predictive modeling.
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