Difference between Machine Learning & Deep Learning

 Machine Learning(ML) is an approach or technique to model a framework which would be able to predict some results on some future data(testing dataset) based on the patterns that the model analyzes in the data already available(training dataset)

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• Machine Learning is basically a subset of Artificial Intelligence which is an idea where we attempt to replicate a human or make and train a Machine to do some job how a human is supposed to do the same. AI being vast we should always have some point to start or begin with. So we kick start with Machine Learning because it's better to start with white box models than black box models. Most of the diagnostic models are also covered in this phase. Equation based modeling helps on the interpretability and explainility which makes it widely accepted for usage and scaling.

• Deep Learning is like a form of Machine Learning only. The only difference is that in DL,

The dataset that we are provided with is in unstructured form i.e., not available in the form of a data frame (or rows and columns) such as images, audio, video etc. With these kinds of datasets, DL is the better suited form of an ML model to go for working on the problem statement and building the model. This also invites the cognitive approach as to how a brain works and the similar approach is applied for machines to work on the same front. The whole neuron architecture is overlaid and brought into action while machines are trained to learn with varying parameters.

• Data can be structured as well as unstructured to work on and develop an ML model solving some underlying problem statement, e.g., heart disease prediction, car prices prediction are some problem statements where data is available in structured form. However, with the problem statements like, Object Detection, Cats-Dogs classification, Image Colorization etc., we’re provided with images rather than simple numerical or characters data. So, we have to employ some Data processing enhancement libraries also in some cases to work with those kinds of data and convert them into numbers for our DL model to train on them. Any form of data that is following a schema is a structured database. Usually the traditional framework is database oriented stuff. Non schema is file structure where we use files and not data. It could be a pool of files with different extensions.

Most of the people working or aspiring to work in this industry have no clear understanding of Machine Learning and Deep Learning and they call themselves data scientists. The challenge is no one is willing to explain on a crystal clear note as so to what is the real difference. The difference could easily be found out from professionals working in this space. But the challenge is how many come out in the open and provide their insights or opinions. We at Industry360 are the best institute for data science in delhi ncr because we have collaborated with experienced Industry professionals who have not only created the content but deliver the same for better intent and extended support . Our online analytics courses in India are used by corporate teams for employee learning & development.