Machine Learning Algorithms and Applications for Data Scientists

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Data scientists are professionals with expertise in different interdisciplinary skills like Machine Learning, data mining, and statistics. Data Science professionals need to learn the application of multiple ML algorithms to solve various types of problems as only one algorithm may not be the best option for all issues. You can join a Machine Learning Bootcamp to gain competency in using frequently applied Machine Learning algorithms.

Top Machine Learning Algorithms in Data Science

Below are the most important ML algorithms that Data Scientists can learn in Machine Learning Training:

  • Linear Regression: Regression analysis is a method of evaluating and determining the relationship between dependent variables and data sets. It tackles the regression problems, while logistic regression tackles the classification problems. Linear regression is an old and most popularly used ML algorithm that Data Science professionals often use.
  • Decision Tree: As its name suggests, a decision tree refers to the arrangement of data in the form of a tree structure. Data gets separated at every node into different branches of the tree structure. The data separation happens according to the attributes’ values at the nodes.
  • Logistics Regression: Logistic regression implies a statistical process for building ML models where the dependent variable is binary. Data Scientists leverage Logistics Regression to describe data and the relation existing amongst a dependent variable and independent variables.
  • Naïve Bayes: It is a set of supervised learning algorithms based on the Bayes Theorem used in various classification problems. Naïve Bayes models are best suited for high-dimensional datasets.
  • K-Means: K-Means is an unsupervised learning algorithm that resolves clustering problems. In this method, data sets are classified into clusters in a way that all the data points within a cluster are heterogeneous and homogenous from the data in the other clusters.
  • SVM Algorithm: The SVM algorithm is a classification algorithm wherein you plot raw data as points in the n-dimensional space. Each feature’s value is tied to a particular coordinate that simplifies data classification. Lines called classifiers are used to split the data and plot them on the graph.
  • KNN Algorithm: This algorithm can be applied to both regression and classification problems. It is a widely used algorithm in the Data Science industry. KNN Algorithm stores all available cases and splits the new ones based on its k neighbours’ majority vote.

Machine Learning training has a well-defined and structured curriculum that imparts knowledge of all these sought-after ML algorithms. You will learn to apply these algorithms while working on case studies and capstone projects under the assistance of Data Science and Machine Learning professionals.

Join SynergisticIT, the best coding bootcamp to become proficient in using Machine Learning algorithms required to start a Data Science career. They offer an immersive Machine Learning Bootcamp training centered around the core and advanced ML concepts, including Decision Tree, Linear Regression, Random Forest, Logistics Regression, Naïve Bayes, NLP, Deep Learning, data analysis, model deployment, tableau, data visualization, etc. So, kickstart your career today.

Source: https://bestmachinelearningca.hatenablog.com/entry/machine-learning-algorithms-and-applications-for-data-scientists

Location: United States

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