5 Must Learn Algorithms for Machine Learning Engineers


There is no doubt how the machine learning area establishes grounded conclusions based on a big amount of data -- the same big data that drives decision-making processes of different industries. This is also the same data that is responsible for gearing up tech companies to be on the next level. And as machine learning engineers, there are certain algorithms that will be helpful for you to learn. We have gathered five algorithms that can boost your credentials.


Linear Regression

You usually encounter linear regression while doing a statistics-based research study. In this algorithm, you graph the independent and dependent variables in a single line. Your data does not need to be in sync with the line that you have drawn. The dots can be placed anywhere around the graph. But the catch is that this algorithm forms the dots from lower left to upper right or vise versa to obtain the linear line.

Logistic Regression
Logistic Regression will be beneficial if you wish to generate a binomial outcome. The algorithm also supports the outcome via explanatory variables. In other words, you draw discrete values; for instance, the binary values form a number of independent variables. Software engineers usually use this through anticipating revenues of a product, predicting earthquakes, and identifying success rates of product campaigns.

Decision Trees
Decision Trees works great in terms of decision-making processes for the company. From the name itself, the graph looks like a tree without its leaves and roots. This allows data analysts to categorize all the important points that you need to review properly. You can group them depending on the level of problem or type of concern it is addressing. It’s good for the decision-making process since the algorithm is limited with a yes or no answer.

Ensemble Methods
Ensemble Methods help you with setting up classifiers and define data points. These learning algorithms also enable you to form data points based on the predictions of your data. You may also implement these in correcting output coding and boosting. Data scientists support Ensemble Methods due to its ability to balance the biases in data gathering, reduction of data diversity, and avoiding over-fitting information.

K-means serve as an example of an unsupervised algorithm. This enables you to group your data sets into different clusters. It is vital for your job to learn this since it determines the centroid of your data sets. Then after determining the centroid, k-means builds a cluster to the closest centroid. The process will continue to happen until the centroids do not change.

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