I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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Key skills required for Machine Learning jobs

By shiwaneeg |Email | Apr 6, 2018 | 15252 Views

Tech improvement all across the industries have helped to develop Artificial Intelligence (AI) for advancement of businesses today. And, Machine Learning(ML) is a branch of AI. So, if we basically talk about Machine Learning (ML),it actually 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. The process of ML is somehow similar to that of Data Mining. Both search through data to look for patterns. But, ML uses the data to improve the program's own understanding. ML programs are used to detect patterns in data and adjust program actions accordingly.

And, if we talk about the skills required for ML jobs. So, An eligible candidate for ML jobs should have a deep understanding of a broad set of algorithms and applied mathematics, problem solving and analytical skills, probability and statistics and programming languages such as Python/C++/R/Java. 

Here are some of the key skills required to land up in ML jobs.

1. Python/C++/R/Java: 

If a candidate wants a job in Machine Learning, he will probably have the knowledge of all these languages. 
C++ can help in speeding code up, whereas R works great in statistics and plots, and Hadoop is Java-based, so it is important to implement mappers and reducers in Java.

2. Probability and Statistics: 

Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. The candidates are required to have a deep understanding of Probability and Stats to understand these models. And, he must be able to use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.

3. Applied Math and Algorithms: 

The candidate must have a firm understanding of algorithm theory. Also he should know how the algorithm works. Then, he is able to discriminate models such as SVMs. He must be able to understand subjects such as gradient decent, convex optimization, lagrange, quadratic programming, partial differential equations and alike. Nevertheless, the candidate is advised to get used to looking at summations.

4. Distributed Computing: 

Most of the time, machine learning jobs entail working with large data sets these days. One cannot process this data using 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.

5. Expanding the Expertise in Unix Tools: 

The candidate should master all of the great unix tools that were designed for this: cat, grep, find, awk, sed, sort, cut, tr, and more. Since all of the processing is most likely on linux-based machine, there is a great requirement for access to these tools. Also, the candidate is advised to learn their functions and utilize them well. 

Other than the technical aspects, the candidate must stay up to date with any upcoming changes and trends. This includes being aware of the news regarding the development to the tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.). He/She must be well acquainted to the habit of reading papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data, etc. Also, there are great free machine learning books online which can accelerate the candidate's knowledge in ML.

Source: HOB