How to Select Suitable Machine Learning Algorithm for a Problem Statement?

This is the blog post by Anolytics explaining how to select suitable machine learning algorithms for a problem statement. Actually, there are multiple types of machine learning process, AI model are trained and developed for predictions. So choosing the right algorithm is important to solve such problems.

The bog post discus about different types of machine learning tasks according to that algorithms are choose. And why choosing these machine learning algorithms are suitable for a particular problem statement. Machine learning developers can find here the types of machine learning tasks.      

Different Types of Machine Learning Tasks:

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

Along with the machine learning tasks, Anolytics in this blog post also discuss about the most common algorithms used in machine learning. Machine learning algorithms are actually, selected as per the model compatibility, need of problem statement and availability of machine learning training data. Each algorithms has its own advantages and limitations for a problem statement.  

Most Common Algorithms Used in Machine Learning:

  • Linear Regression and Linear Classifier
  • Logistic Regression Algorithm
  • K-means
  • Neural Networks
  • Decision Trees Algorithm
  • Principal Component Analysis (PCA)

And as per the machine learning algorithms, training data sets are required to train the model. And Anolytics provide the high-quality training data sets for visual perception based AI development. It is offering image annotation services to annotate the different types of objects in image annotations. Using the wide range of annotation techniques it can provide the training data sets for different algorithms to develop a right and more practical machine learning model at lowest cost.

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