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Could anyone tell me how practically relevant are classical machine learning algorithms like SVMs or decision trees as compared to deep learning?

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With the development and use of Deep Learning and its methodologies, the past techniques and methods cannot be simply dusted and forgotten. It is obvious that with new technology in the picture that makes several tasks easier, no one would prefer working hard and using complex mathematical and statistical concepts like linear algebra, convex optimization, multivariate statistics, etc., to understand complex data.

Although it may not seem like it, the most used methods and techniques in various business projects that help in revenue generation are traditional statistics and Machine Learning methods instead of Deep Learning. Decision trees, linear programming, logistic regression, bandit algorithms, SVMs, random forests, and linear statistical methods, are the core foundation of generating revenues in an organization. Hence, classical Machine Learning techniques and algorithms and comparatively more practically relevant than Deep Learning techniques.

So, in order to pursue a career in the field of Machine Learning, you must enroll in our Machine Learning Course wherein the curriculum includes both traditional Machine Learning algorithms and Deep Learning techniques in detail. This training program is conducted by experts from top MNCs around the world with over 12 years of working experience. They will help you gain theoretical and practical knowledge of the numerous algorithms and techniques and help you gain hands-on experience through projects. This will come in handy while applying for jobs and going for Machine Learning job interviews.

To clear your doubts on Machine Learning and Deep Learning, you can also watch this video tutorial below:

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