**Would you be surprised if we told you that over 50,000 job vacancies in Data Science and Machine Learning remain unfulfilled in India?** Considering the fact that Machine Learning is one of the hottest career fields right now, this may seem shocking, but it is the hard truth. Do you know the reason behind the demand-supply paradox of professionals in Data Science and ML?

It is solely because there aren’t enough skilled and talented candidates ready to take on the booming job opportunities in these emerging fields. Gartner maintains that among the 10 lakh registered firms in India, as high as 75% have already invested or are ready to invest in Machine Learning. **Clearly, job opportunities in Machine learning are bound to increase exponentially in the near future. The need of the hour is “upskilling” to fit the requirements of ML job profiles.**

**Skills required to land Machine Learning jobs**

**Fundamental knowledge of Computer Science and Programming**

To build a successful career in ML, you must first you need to have an in-depth understanding of the fundamental concepts of Computer Science including Data Structures (stacks, queues, trees, graphs, multi-dimensional arrays, etc.); Computer Architectures (memory, cache, bandwidth, distributed processing, etc.); Algorithms ( dynamic programming, searching, sorting, etc.), and Computability & Complexity (big-O notation, P vs NP, NP-complete problems, approximate algorithms, etc.), to name a few.

Once you understand these, you must learn how to employ and implement them while writing code. As for choosing a programming language, you can begin with Python. It is great for beginners and is the lingua franca of Machine Learning. You can hone your programming skills by taking part in online coding competitions and hackathons.

**A strong rapport with Probability and Statistics**

Statistics and probability concepts form the core of numerous ML algorithms. Naturally, it is imperative to have a strong knowledge and understanding of statistical concepts including Mean, Median, Variance, Derivatives, Integrals, Standard Deviations, etc.; Distributions (uniform, normal, binomial, etc.), and the various analysis methods (ANOVA, hypothesis testing, etc.) that are essential both for developing data models and validating them. Apart from statistical flair, you must also understand the fundamentals of probability like Bayes rule, likelihood, independence, Bayes Nets, Gaussian Mixture Models, Markov Decision Processes, Hidden Markov Models, and so on.

**Experience in Data Modeling and Evaluation**

One of the primary goals of Machine Learning is to analyze vast amounts of unstructured data. To do this, you must know the art of Data Modelling. Data Modeling is the technique of estimating the underlying data structure of a particular dataset to unravel and identify the hidden patterns within (clusters, correlations, eigenvectors, etc.) and also predict the properties of instances never seen before (classification, regression, anomaly detection, etc.).

During the Data Modelling process, you will be required to choose appropriate accuracy/error measures (for instance, log-loss for classification, sum-of-squared-errors for regression, etc.) and evaluation strategies (training-testing split, sequential vs randomized cross-validation, etc.). So, before you start applying algorithms, you need to gain a thorough understanding of the basic concepts involved in in the Data Modelling.

**Possess Software Engineering skills**

Whether you are a Data Scientist or a Machine Learning Engineer, you need to possess the typical Software Engineering skills and knowledge base. If you have a Software Engineering background, great! If you don’t, you need to learn about the best practices in Software Engineering, including system design, modularity, version control, code analysis, requirements analysis, testing, documentation, among other things. The following step would be to learn how these concepts function together in the development of system interfaces. Understanding the nitty-gritty of system design is essential to prevent the occurrence of bottlenecks in the process.

**Learn how to apply ML Algorithms and Libraries**

There are a host of libraries/packages and APIs that contain the standard implementations of ML algorithms such as Scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc. However, the secret to making the most out of them is to know how to apply them effectively on suitable models (neural nets, decision trees, nearest neighbour, support vector machine, etc.). Not just that, you must also be familiar with the learning procedures (linear regression, gradient descent, genetic algorithms, boosting, etc.) that fit the data at hand.

The best way to get familiar with ML algorithms, libraries, and how to apply them correctly is to take up online challenges in Data Science and Machine Learning.

**Get familiar with Advanced Signal Processing techniques**

Feature extraction is one of the core essences of Machine Learning. Depending upon the problem at hand, you have to perform feature extraction using appropriate advance signal processing algorithms like wavelets, shearlets, curvelets, contourlets, bandlets, etc. Simultaneously, you must also learn about the various analysis techniques such as Time-Frequency analysis, Fourier Analysis, Convolution, etc.

**Never stop upskilling and learning**

As you know, Machine Learning is still an evolving discipline, with time new ML concepts, algorithms, and technologies will develop. To keep pace with the changing times, you must continuously upskill and develop new skill sets. This would involve staying updated with the latest tech and Data Science trends, working with new tools and theories, reading scientific journals, staying active in various online communities, and much more. Long story short, you should always have the urge to learn new things.

**To conclude,**

The applications of Machine Learning have already begun to intertwine in our lives in ways that we couldn’t imagine before. Healthcare, education, finance, business – you name it, Machine Learning is everywhere. As long as the world continues to churn data, Machine Learning will reign, and with time, help us find answers to the most complicated real-world scenarios. The change has begun – it’s time you brace yourself for the new future with Data Science and Machine Learning.

So, begin today and start acquiring these Machine Learning skills!

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