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. https://www.synergisticit.com/machine-learning-training-bay-area-ca/

1. SynergisticIT
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2. Machine Learning Algorithms and
Applications for Data Scientists
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
● 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.

3. • 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.

4. • 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.

5. 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.

6. 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

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