Skills After Completing A Machine Learning Course
Today, with the growth which the IT industry has forsaken over the years, there are a lot of things that a lot of people need to ponder. Right from the education industry to the retail sectors and factories, there are a lot of factors that hold the criteria in which the industry are depending upon. For a career in machine learning, a fancy degree is not always required. According to some research analysts of a giant digital marketing agency, the salary of an entry-level machine learning expert will hover around 6 LPA-8 LPA. With the expense of time, and a worthwhile increase of experience, a particular machine learning could take a giant leap and it can go around 18 LPA.
Machine Learning, with the advent of technology, and the dominion of software industries shown by the other technological factors has itself taken a few over the top and unimaginable leaps, making its presence known in the dominion of software technologies. Frankly speaking, a lot of youths are actually venturing into the software industry, keeping machine learning in mind. Seeing the growing number of students who are free-flowing in the software industry, a lot of changes were made in the education sector and now, an amazing number of industries, today, is giving machine learning online courses that are giving training to the aspirants.
But did the students have taken the right decision in selecting machine learning as their career choices? If this is the matter, let us dive deep and see what are the skills which get developed after the completion of a particular machine learning course.
A few of them are:
- Programming Skills
Learning languages will only make the concepts clear. Learning C++ will make the speed of the code better.For plotting and statistics purposes, the best part is to do the concepts with the help of R. Talking about Hadoop which is Java-based, implementation of mappers and reducers will be done through this skill.
- Probability and Statistics
Some important concepts which are a part of this domain, are:
- Naive Bayes
- Gaussian Mixture Models
- Hidden Markov Models
Apart from this, some important methodology, which is being used by a machine learning expert are:
- Confusion Matrices
- Receiver Operator- Curves
- Data Modelling & Evaluation
A few concepts where a machine learning expert should be using his expertise are:
1. log-loss for classification,
2. sum-of-squared-errors for regression
3. Evaluation strategy (training-testing split,
4. sequential vs. randomized cross-validation
- Algorithms For Machine Learning
Since, studying Machine Learning will require the exposure of algorithms, where one needs to apply the algorithms, depending upon the task pondered. Mostly, Machine Learning experts require different platforms where various algorithms for machine learning are inserted. Some are:
1. Gradient descent
2. Convex optimization
3. Quadratic programming
4. Partial differential equations
- Optimize Yourself With Changing Time
1. Read a lot of books
2. Go through a lot of research papers
3. Develop yourself with the development of tools
4. Machine Learning Catalogs
5. Video Tools
6. Go through video references and also industrial conferences.
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