Artificial Intelligence Expert

Top Machine Learning Algorithms an Artificial Intelligence Expert Must-Know

Machine learning has become the tech du jour for technology professionals, especially in the present era where artificial intelligence (AI) is taking over the world.

After all, machine learning is the key driver that enabled self-driving cars, practical speech recognition, or an advanced understanding of the human genome.

Because of its expertise in making predictions just by observing past experiences, machine learning technology is now used by multiple industries. As an artificial intelligence expert, one needs to know different ML algorithms along with their purposes.

Let us take a tour and categorize the top machine learning algorithms you might need to master.

Logistic regression

Renowned algorithm ideal for estimating discrete values i.e. binary values, 0,1 taken from a set of independent variables. The algorithm helps predict any type of probability of any event, and this is done by simply fitting the data to a logic function, thus called the logit model.

The below-mentioned methods often help boost logistic regression models –

  • Eliminating features
  • Usage of the non-linear model
  • Interaction terms
  • Regularize techniques

Linear regression

Linear regression is a linear model most frequently used in the statistical technique. It also one of the most understood machine learning algorithms used in machine learning projects. This method attempts to model two variables by fixing a linear equation for observing data. One of the variables is to be considered as the explanatory variable while the other will be considered a dependent variable.

For instance, an artificial intelligence expert will want to use a linear regression model to relate the weights of individuals to their respective heights.

However, before attempting to fix a linear model to the observed data, the modeler needs to check whether there is a relation between those variables of interest or not. A linear regression’s line has an equation –

Y = a + bX, where X is the explanatory variable, and Y is the dependent variable, a is a slope, and b is where it intercepts.

Naïve Bayes

A certain feature in a class is said to be unrelated to the feature of another feature, Naïve Bayes assumes this theorem.

Despite having similar features, the Naïve Bayes classifier will still consider all these properties independently, but while making the final probability calculation of a certain outcome. This model is easy to build and is used in large datasets.

K-Means

The unsupervised algorithm is used for solving cluster problems. For instance, many data sets can be classified into a certain number of clusters, this can be called K. Thus, they are formed in such a manner where all these data points could be heterogeneous and homogenous from the data in the other cluster.

This is how K-means form clusters –

  • It picks a certain number of points called centroids for every cluster.
  • For each data point if forms a cluster which is closest to the centroid, also called K clusters.
  • This is where new centroids are created according to the existing cluster members.
  • So now the new centroids and the nearest distance for every data point is then calculated. The repetitive process continues until these centroids don’t alter their form.

You can easily learn these algorithms through programming and advance your skillset with the help of the best AI certification programs available online.

KNN (K- Nearest Neighbors)

This algorithm can be used for both regression and classification problems. However, in the data science industry, it has been frequently used for solving classification problems. This is one of the simplest algorithms which stores most cases and then classifies them by getting the highest vote of their k-neighbors. Once this happens, the case is then given to another class with the most common feature present in it.

Here’s what you need to take care of before choosing KNN –

  • The variables should be normal or may cause bias in the algorithm
  • Data should be pre-processed
  • It is computationally expensive

 

 

 

 

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