Data Science is a vast field introducing newer technological developments day-by-day, is chosen by many aspiring data scientists. Machine Learning is yet another booming topic in research and industry with ever-growing technologies. 

Certain complexities in machine learning make it challenging for even experienced professionals and overwhelming for beginners. 

However, we have decoded the top 10 machine learning algorithms in the below article, making the subject simple for those who have opted for these core concepts. 

An algorithm is nothing but a model designed for a particular task to perform or even predict the probability of events. It is also called as a mathematical equation represented as a business solution. 

The top 10 machine learning algorithms are as follow: 

Hypothesis testing 

A hypothesis testing is a must-needed and basic algorithm for any data scientist. It is a process in which several statistical tests are conducted to prove whether a hypothesis is true. Based on this, the acceptance or denial of testing is carried out. The hypothesis test is classified into categories like t-test, chi-square test, etc. 

Classification 

It is one of the classes of supervised Machine Learning, which predicts or explains a class value. One of the classic examples for this is that it assesses whether a customer will buy a product. However, this method of analysis doesn’t end with this. It is used for various classifications with several perspectives. 

Linear Regression 

A statistical modeling technique models the relationship between an explanatory variable and a dependent variable by analyzing the observed data points on a linear equation. 

This technique is prominently used when there is a relationship between the variables. They are usually checked by using scatterplots.    

Clustering 

Followed by Linear Regression, the next that secures a place in the top 10 machine learning algorithms is clustering. It comes under unsupervised machine learning as the primary goal is to group or cluster a set of observations. This method does not use the output information for training, but it lets the algorithm define the output. In clustering methods, the visualizations are the only way to inspect the quality of a solution. 

Dimensionality Reduction 

The concept of this algorithm, as the name suggests, is to remove the least important information. As the data sets often come up with hundreds of or even thousands of column, the algorithm – dimensionality reduction is important. 

Some of the popular dimensionality reduction algorithm types include t-Stochastic Neighbour Embedding (t-SNE), Principal Component Analysis (PCA), etc.., 

ANOVA 

The one-way analysis of variance (ANOVA) is prominently used to determine whether the mean of more than two groups are significantly different from each other. 

It works by comparing the variance between the two groups. As a result, finding whether all groups belong to one larger group becomes possible by applying this technique. Besides, it also helps in expelling the data sets with different characteristics. 

Ensemble methods 

As the name suggests, ensemble methods combine several predictive methods to get higher quality predictions. This algorithm is a way to reduce the variance and bias of a single machine learning model. It is important as a model might be accurate under certain conditions but inaccurate under other conditions. 

Transfer Learning 

It refers to re-using a previously used neural net to a new but a similar task. In different terms, once a neural net is trained for a specific task, a fraction of the trained layers shall be combined with a new set of layers. With this, the neural net can adapt to the new task instantly. 

The advantage of this process is that it requires a sparse amount of data to train the neural net. As the time and money required for deep algorithm learning are high, this is a highly preferred technique. 

Reinforcement Learning 

The data scientists can opt for this methodology when there is no historical data about a problem as it does not require information prior. In this method, you will learn as you proceed with the process. This technique is successful in developing games. 

This Machine Learning algorithm is a powerful form of AI, and there is a high scope for progress. 

Decision Trees 

It is a tree-shaped visual representation under which all possible options and the probability of occurrence are put up. They are super-easy to understand and interpret. Each node of the tree represents the decision to be made about a particular task, and the further nodes represent the consequences that will be followed post the decision. Hence, the possibility of differentiating the action plans for each task and finding errors becomes simple. 

Are you confused about the advanced Data Science techniques? Worry not. Ace it like a pro with the expert assistance. Get connected to the professional Data Science & Business Analytics Training near you through Sulekha.