Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
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Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
Machine Learning Overview
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
The statistical approaches include regression, decision trees, Bayesian learning, support vector machines (SVM) and k-means method.
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
Classification Based on Explain-ability:
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
2.3 Evaluating a Machine Learning Model
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
Regression algorithms capture the relation between various input variables and the output variable to build a predictive model.
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
Based on the above description, we can classify machine learning use cases into 2 categories: – Decision support use cases – Cognitive use cases
Suresh Samudrala • Machine Intelligence : Demystifying Machine Learning, Neural Networks and Deep Learning
Number of data records available can also influence the choice of the machine learning algorithm. Machine learning algorithms like support vector machines (SVM) can work with fewer training records whereas neural networks and deep learning algorithms require large amounts of data to be able to train the model effectively.