Machine Learning Algorithms and Applications for Data Scientists

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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.

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• 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.

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• 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
  features 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.

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• 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.

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• 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.c
  om/entry/machine-learning-algorithms-and-applicati
  ons-for-data-scientists

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