The Top 10 Machine Learning Algorithms for ML Beginners

Machine learning has been one of the world’s hottest subjects in the last decade, and Andrew Ng a machine learning expert treats it as the latest electricity. Many of the services we use today’s global machine learning powers-recommendation systems such as Netflix, YouTube, and Spotify; search engines such as Google and Baidu; social media feeds such as Facebook and Twitter; voice assistants such as Siri and Alexa. The list continues.  

Now let’s talk about the most used machine learning algorithms for beginners.

Blog Contents

  • Ten Most Used Algorithms
  • Conclusion

Ten Most Used Algorithms

  1. Linear Regression

We have several input variables (x) in machine learning, which evaluate an output variable (y). There is a relationship between the output variable and the input variables. ML aims to measure this relationship.

The relationship connecting the input variables (x) and the output variable (y) is expressed in Linear Regression as an equation of the form y = a + bx. Therefore the purpose of linear regression is to figure out the values of a and b coefficients. Here the intercept is a, and b is the line’s slope.

  1. Logistics Regression 

After employing loads, logistic regression is restricted to linear regression, including non-linearity (sigmoid function or tanh is mostly used), so the performance limit is similar to + / — groups. Using the method of gradient descent, cross-entropy loss functions are optimized.

  1. Linear Discriminant Analysis (LDA)

Logistic Regression is a division algorithm typically restricted to distribution problems with only a couple of classes. If you have more than two combinations, then the alternative linear classification approach is the Linear Discriminant Analysis algorithm.

The LDA representation is relatively straightforward. It consists of your data’s statistical properties, calculated for each class. This requires a single input variable:

Mean value for each of the classes

The measured variance for all groups

For each class, predictions are made by measuring a discriminating value and predicting the class with the greatest value. The method considers that the data has a Gaussian distribution, so eliminating outliers from your data before is a good idea. It’s a simple and effective approach for problems with predictive modeling classification.

  1. Decision Trees

One of the most popular algorithms for machine learning. Used for statistical models in statistics and interpretation of results. The structure depicts the “leaves” and “branches.” The objective function attributes are based on the “branches” of the decision tree. The objective function values are reported in the “leaves,” and the left nodes include attributes that vary from case to case.

  1. K-Means Clustering 

Everyone’s favorite algorithm for unregulated clustering. Let’s illustrate, though, what clustering is:

The method of dividing up a collection of objects into groups described clusters is clustering. There should be “alike” objects inside each class, and objects from diverse groups should be as diverse as likely. The key differentiation among clustering and classification is that during the algorithm’s operation, the list of groups is not explicitly defined and is calculated.

The k-means algorithm in the traditional .. is the easiest, but likewise, quite an incorrect clustering method. This splits the set of vector space elements into a previously known number of k clusters.

  1. Naïve Bayes

For predictive modeling, Naive Bayes is a simple but surprisingly effective algorithm.

The model consists of two kinds of probabilities that can be directly determined from your training data:

1) The possibility of each class

2) The conditional probability is given by any x value for each class.

Once estimated, the probability model can use the Bayes Theorem to make forecasts for new data. It is normal to assume a Gaussian distribution (bell curve) when the data is real-valued so that you can estimate these probabilities easily.

Naive Bayes is called naive since it assumes the independence of each input variable. This is a strong assumption and impractical for real data, but the technique is beneficial on a broad range of complex issues.

  1. Support Vector Machines 

Maybe one of the most common and discussed machine learning algorithms is Support Vector Machines.

A hyperplane is a line that divides the space of the input variable. In SVM, a hyperplane is chosen such that the points in the input variable space are ideally distinguished by their class, either class 0 or class 1. You can imagine this as a line in two dimensions, and let’s say that this line can isolate all of our input points. The SVM learning algorithm seeks the coefficients that result in the hyperplane being the best to distinguish the groups.

  1. Random Forest and Bagging

One of the most common and most efficient algorithms for machine learning is Random Forest. It is a kind of algorithm for ensemble machine learning called Bootstrap Aggregation or bagging.

A significant statistical method for estimating a sum from a data sample is the bootstrap. Such as the median. To give you a more reliable estimate of the true mean value, you take lots of samples of your results, measure the mean, then average all of your mean values.

  1. Apriori

In a transaction database, the Apriori algorithm is used to mine frequent itemsets and then create association rules. In market basket analysis, it is widely used to look for combinations of items that sometimes co-occur in the database. In general, we write the association rule as if a person buys item X; then he believes item Y’ as X-> Y.

  1. K-Closest Neighbors

There is a straightforward and very efficient KNN algorithm. For KNN, the model representation is the whole training dataset. Easy, huh?

By searching through the entire training set for the most similar K instances (the neighbors) and summarising the output variable for those K instances, predictions are made for a new data point. This could be the median output variable for regression problems. This could be the mode (or most common class value for classification problems.


Before trying various algorithms, even an accomplished machine learning expert can not say which algorithm would work the best. Although there are several other algorithms for Machine Learning, these are the most common ones. This will be a good starting point for learning if you’re a newbie to Machine Learning course.