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What is deep learning We've covered a number of different basic concepts in machine learning, you know what supervised and unsupervised learning techniques are, you understand basic machine learning use cases such as regression, classification, and clustering and you know how these models work at a very high-level and you know what metrics you can use to evaluate these models. We've also discussed traditional machine learning models, and we've discussed the basic structure of some of these models, specifically linear regression, logistic regression, and decision trees. It's time for us to turn our attention to the most powerful models that we have today, deep learning models, models built using neural networks. What's Deep learning model: But first, let's understand the difference between traditional models vs. deep learning. Traditional models fit an algorithmic structure on data we saw which either fits a line on data or a curve or a tree, and it uses this alg...

Photo source: pexels.com In this article, we'll explore the critical features of machine learning. Along with the comparable features of machine learning and deep learning. We can utilize the capabilities afforded by machine learning to resolve various categories of problems. Now, machine learning utilizes various approaches in order to resolve and provide solutions. Some of the approaches adopted by machine learning to resolve problems includes Classification, Clustering, Forecasting, Simulation, Regression, and Optimization. With the Classification approach, the application learns from the input data. And then utilizes what it has learned to classify newly identified observations. We can utilize Clustering algorithms to classify each data point into a specific group, depending on the similarity of the identified attributes. Now the objective of the forecasting algorithms provided in machine learning is to achieve accuracy of data against time. Simulation provides a view of simula...