Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018.
With technology such as Machine learning, AI, and predictive analytics reshaping the business landscape, software products, aggregators, Fintech, and E-commerce will drive the demand for technology professionals in India.
Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.
Now, are you trying to understand some of the skills necessary to get a Machine Learning job? A great candidate should have a deep understanding of a broad set of algorithms and applied math, problem-solving and analytical skills, probability and statistics, and programming languages.
Here is a list of key skill sets in detail:
Programming Languages like Python/C++/R/Java
If you want a job in Machine Learning, you will probably have to learn all these languages at some point. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need to implement mappers and reducers in Java.
Probability and Statistics
Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have a firm understanding of Probability and Stats to understand these models. Use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.
Data Modeling & Evaluation
A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.)
Machine Learning Algorithms
Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient descent, convex optimization, quadratic programming, partial differential equations, and alike.
Most of the time, machine learning jobs entail working with large data sets these days. You cannot process this data using a single machine, you need to distribute it across an entire cluster. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective.
Advanced Signal Processing Techniques
Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advanced signal processing algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets.
You must stay up to date with any up and coming changes. It also means being aware of the news regarding the development of the tools (changelog, conferences, etc.), theory, and algorithms (research papers, blogs, conference videos, etc.).
Read a lot:
Read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.
The next question you would have is, “What can I do to develop these skills?” Unless you already have a strong quantitative background, the road to becoming a Machine Learning Specialist will be a bit challenging – but not impossible.
However, if it’s something you’re sincerely interested in and have a passion for Machine Learning and lifelong learning, don’t let your background discourage you from pursuing Machine Learning as a career.
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