Looking to Build a Career in ML? Have a Strong Grip in These Skills

by Miles Education Make an impact. Lead,Excel, Serve

Are you planning to take off a career in Machine Learning (ML)? Well, then this article is just for you. Building a career in ML is not rocket science but requires a dedicated approach.

Besides ML, emerging technologies like Artificial Intelligence (AI), Data Science, and Cloud Computing are picking up pace today. That said, many are not much aware of the real scope and implications of these technologies but rather used them as a buzzword. This is one of the reasons why you need to hone ML to make great strides in your career journey.

Fundamentals and Programming Knowledge

These are paramount for any aspiring ML candidate. You must be aware of various computer science (CS) concepts, such as data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), and space and time complexity.

If you are a computer science graduate, then the above concepts can become a vantage point for your future career. Further, having a knack at multiple programming languages, including R and Python for ML and statistics, Hadoop and Spark for distributed computing, and SQL for database management, is necessary to survive the competitive market.

Applied Mathematics and Statistics

Maths is an essential skill in the arsenal of an ML professional. But thinking, why need math if it is not your favourite? Well, math has multiple uses in ML. You can apply numerous mathematical formulae to choose the suitable ML algorithm for your data, set parameters, and estimate confidence intervals.

Many of the ML algorithms are applications based on statistical modeling methods. So, understanding them is a cakewalk if you have a strong foundation in Maths. Some critical math concepts you need to know may include probability, linear algebra, multivariate calculus, statistics, and distributions — Poisson, normal, binomial.

Data Modeling and Evaluation

As an ML student, you need skills in data modeling and evaluation. After all, data is your money-making engine. Moreover, you need to analyze the data using an algorithm that is compatible with the data. For instance, the kinds of ML algorithms to leverage classification, regression, and clustering rely on data. A classification algorithm well-suited to extensive data and speed might be Naive Beyes, or a regression algorithm for accuracy may be a random forest. You must deep dive into all these details about different algorithms to contribute to data modeling and evaluation effectiveness.

Natural Language Processing

Natural Language Processing (NLP) is inherently a critical and fundamental aspect of ML. There are myriads of libraries that offer the foundation of NLP. These libraries contain numerous functions that are utilized to make computers understand natural language by splitting the text based on its syntax, pulling out the vital phrases, and removing irrelevant words. You can learn a few or even one of these libraries, including the Natural Language Toolkit — the most prevalent platform for developing NLP-based applications.

Communication Skills

While you understand the data extracted using ML better than anybody else, it’s equally crucial to communicate these insights to a non-technical crew, your clients, or stakeholders. Also, this can involve data storytelling, where you need to present your data in a storytelling pattern with a beginning and ending based on concrete results. And if you can communicate these insights, the sky’s your limit in your profession.

Start Acting Towards It

Every field today is leveraging the potential of ML, be it cybersecurity, medicine, or automobiles. As such, learning more about ML and developing a relevant skill-set to succeed is an excellent idea and might even be a pretty wise career move. So, take a look at all these skills and begin learning them from day one to capitalize on your capabilities and bag your dream job in ML.

Over 70% across various firm categories expect their headcount to increase in response to AI over the next two years. If you aspire to boost your career prospects and learn AI-ML applications, Miles in collaboration with IIT Mandi and Wiley is offering a PG certification in Applied AI and ML. This course will walk you through Deep AI processing and industry-specific tools for the entire AI lifecycle. Plus, you will learn to ‘Apply AI’ in real-world scenarios under the guidance of the top industry experts of the Wiley Innovation Advisory Council.