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What should I expect from Advanced Certificate Program in Machine Learning by IIT Delhi?
What are the topics that will be covered in the program?
Can I sign up only for a few specific modules if I am interested in?
What type of learning experience should I expect?
Is there a WES equivalency for program?
The latest sensation in this field is machine learning in the cloud. It is concerned with taking machine learning capabilities up to the cloud and making them more easily available and accessible. Solving problems for different industries mainly:
1. Knowledge gap, wherein the in-house team isn’t ML or data science proficient.
2. Scalability. Using ML for small projects is easy. But the same cannot be said for larger ones and this extends to both hardware and software.
3. Costs. As the size and scope of projects increases, more investment is needed for hardware and software to execute more complex algorithms.
The solution to all these problems comes with taking machine learning to the cloud.
1. The knowledge gap is bridged because ML in the cloud doesn’t require very deep skills or hardcore knowledge.
2. Scalability becomes easy since the cloud is limitless in a sense and ample space is available for an everyday business to meet its requirements.
3. Taking ML to the cloud is cost-efficient since it operates on a pay-per-use basis. Companies pay for what they use.
Machine learning in the cloud is, thus, the technology of the future. More and more companies will adopt it and choose it when they want to benefit from ML and AI capabilities. And when this happens, they will need professionals who can work well with ML and in the cloud as well.
The main focus of machine learning is to help organizations enhance their overall functioning, productivity, and decision-making process by delving into the vast amounts of data reserves. Apart from this upfront benefit, machine learning has the following advantages:
1. Timely Analysis And Assessment: By sifting through massive amounts of data such as customer feedback and interaction, ML algorithms can help you conduct timely analysis and assessment of your organizational strategies.
2. Real-time Predictions Made Possible Through Fast Processing: One of the most impressive features of ML algorithms is that they are super fast, as a result of which data processing from multiple sources takes place rapidly. This, in turn, helps in making real-time predictions that can be very beneficial for businesses.
3. Transforming Industries: Machine learning has already started to transform industries with its ability to provide valuable insights in real-time. Finance and insurance companies are leveraging ML technologies to identify meaningful patterns within large data sets, to prevent fraud, and to provide customized financial plans for various customer segments.
So, there are opportunities galore for machine learning engineers, and the indispensable nature of their jobs ensures that machine learning salaries in India are consistently elevated.
To make your career to grow as a machine learning engineer and earn the highest possible machine learning salary in India, you need to have update yourself with:
1. Statistics and probability: Your understanding of algorithms has a lot to do with your knowledge of theories. Hidden Markov models, Naïve Bayes, and Gaussian Mixture, amongst other models, are great examples.
2. Data evaluation and modeling: You can use different methods, such as regression and classification, to measure the error or accuracy of a given model. You also need an evaluation strategy to go with it.
3. ML algorithms: Knowing how different machine learning algorithms work and which algorithm fits in a particular situation is very important. You would need to develop an understanding of subjects like partial differential equations, gradient descent, quadratic programming, convex optimization, and others.
4. Programming languages: For a career in machine learning, you also need to be skilled in different programming languages, such as Java, R, Python, and C++.
5. Signal processing techniques: Feature extraction is very important to machine learning. You may have to work with several advanced signal processing algorithms, including bandlets, shearlets, contourlets, and curvelets, amongst others.