5 Must Have Skills Needed For Machine Learning Jobs

October 5, 2019 Leave a comment Featured

Machine Learning has emerged to be a synonym for our planet’s future. 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, Machine learning, on the other hand, focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data. With such powerful technological developments reshaping the business landscape, various companies have become an active host for a pool of candidates to work day in and out in such fields of work.

Now that you have planned to poach such a wide industry like Machine learning and expect to find a good job opportunity in terms of remuneration and job satisfaction, one must remember some advanced yet handy skills that would assist them and improve their work efficiency.

  • Programming Languages like Python/C++/R/Java- Programming languages are used while programming to feed in and implement algorithms. With thousands of different programming languages being created every year, some of the traditional ones like Python, C++, Java etc. are taught as a curriculum while studying about machine learning. C++ can help in speeding the coding process up while R works great in statistics and plots, and Hadoop works to implement mappers and reducers in Java.

  • Data Modeling & Evaluation- The key part towards the successful implementation of this software is continuous evaluation of how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure like log-loss for classification, sum-of-squared-errors for regression, etc. and an evaluation strategy like training-testing split, sequential vs. randomized cross-validation, etc.to get the best results. Other than that, theories can also help in learning about algorithms. Some great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. To have a firm understanding about these models one must know their probability and stats like a pro.

  • Machine Learning Algorithms- Having a deeper understanding of the algorithm and how it works is a necessity. While you get to differentiate among various terms like gradient decent, convex optimization, quadratic programming, partial differential equations and alike etc., you also get to gain a thorough understanding about various algorithms used in machine learning like Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms and Gradient Boosting algorithms. All these algorithms are based on supervised, unsupervised and reinforcement learning.

  • Distributed Computing- Distributed computing is a field of computer science that studies components which are located on different networked computers and communicate and coordinate their actions by passing messages to one another, also known as distributed systems. These components’ major function is to interact with one another to achieve a common goal. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications. Most of the time, machine learning job opportunities entail working with large data sets for days. You cannot process this data using a single machine, you need to distribute it across an entire cluster of machines to get it right. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective to process and implement any such program.

  • Staying updated- Staying up to date with any upcoming changes in your field of interest is always a positive. Not just that but being aware of news regarding the same helps you stay on top of your game 24×7. Reading papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data etc.is a good way to understand the latest trends and devise any plans accordingly.

If machine learning is something that one is passionate then there is no one stopping them but one just must be prepared for a lifetime of learning as this field is still in its blooming phase and will keep growing in the near future. Even though the entire industry is still a new concept in the market, the continuous competition must be expected. Hence keeping yourself efficiently up to the mark and proving your worth to the company they are affiliated with is the key to sustain. Other than that one can always develop new skill sets and polish up on the old ones.

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