Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow icon6 Machine Learning Skill Sets That Can Land You in a Perfect Job

6 Machine Learning Skill Sets That Can Land You in a Perfect Job

Last updated:
18th Aug, 2019
Views
Read Time
5 Mins
share image icon
In this article
Chevron in toc
View All
6 Machine Learning Skill Sets That Can Land You in a Perfect Job

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?

Best Machine Learning and AI Courses Online

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.

In-demand Machine Learning Skills

Ads of upGrad blog

Skills required to land Machine Learning jobs

1. 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.

Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

Ads of upGrad blog

Popular AI and ML Blogs & Free Courses

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!

Profile
Prashant Kathuria is currently working as a Senior Data Scientist at upGrad. He describes himself as a data freak and others working with him will agree. Working in Data since more than 3 years in Product companies has taught him that data of today is gold of tomorrow. You will find him brainstoring about new things, or reading about upcoming technologies in his free time.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Explore Free Courses

Suggested Blogs

15 Interesting MATLAB Project Ideas & Topics For Beginners [2024]
82075
Diving into the world of engineering and data science, I’ve discovered the potential of MATLAB as an indispensable tool. It has accelerated my c
Read More

by Pavan Vadapalli

09 Jul 2024

5 Types of Research Design: Elements and Characteristics
47006
The reliability and quality of your research depend upon several factors such as determination of target audience, the survey of a sample population,
Read More

by Pavan Vadapalli

07 Jul 2024

Biological Neural Network: Importance, Components & Comparison
50465
Humans have made several attempts to mimic the biological systems, and one of them is artificial neural networks inspired by the biological neural net
Read More

by Pavan Vadapalli

04 Jul 2024

Production System in Artificial Intelligence and its Characteristics
86681
The AI market has witnessed rapid growth on the international level, and it is predicted to show a CAGR of 37.3% from 2023 to 2030. The production sys
Read More

by Pavan Vadapalli

03 Jul 2024

AI vs Human Intelligence: Difference Between AI & Human Intelligence
112802
In this article, you will learn about AI vs Human Intelligence, Difference Between AI & Human Intelligence. Definition of AI & Human Intelli
Read More

by Pavan Vadapalli

01 Jul 2024

Career Opportunities in Artificial Intelligence: List of Various Job Roles
89124
Artificial Intelligence or AI career opportunities have escalated recently due to its surging demands in industries. The hype that AI will create tons
Read More

by Pavan Vadapalli

26 Jun 2024

Gini Index for Decision Trees: Mechanism, Perfect & Imperfect Split With Examples
70599
As you start learning about supervised learning, it’s important to get acquainted with the concept of decision trees. Decision trees are akin to
Read More

by MK Gurucharan

24 Jun 2024

Random Forest Vs Decision Tree: Difference Between Random Forest and Decision Tree
51695
Recent advancements have paved the growth of multiple algorithms. These new and blazing algorithms have set the data on fire. They help in handling da
Read More

by Pavan Vadapalli

24 Jun 2024

Basic CNN Architecture: Explaining 5 Layers of Convolutional Neural Network
269730
Introduction In the last few years of the IT industry, there has been a huge demand for once particular skill set known as Deep Learning. Deep Learni
Read More

by MK Gurucharan

21 Jun 2024

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon