Machine Learning: A Guide for Better Career

Machine Learning is a smart option for career growth ahead of the tide for today’s technology. For college students, when facing various career options upon graduation. So, it could be overwhelming to choose a job that better fits with his/her future career goals

Machine Learning: A Guide for Better Career

Machine Learning is a smart option for career growth ahead of the tide for today’s technology. For college students, when facing various career options upon graduation. So, it could be overwhelming to choose a job that better fits with his/her future career goals

As an industry, machine learning is one that’s experiencing exponential growth with incredible potential for the future – and machine learning for career growth is important to consider.

With 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations. These recommendations are an outcome of the customer’s browsing and purchasing behaviour almost instantly. Hence, No humans could do that.

Machine Learning a Career Landscape​

According to the recent LinkedIn‘s Emerging Jobs report, Artificial Intelligence had a significant presence in the top emerging jobs in 2018. AI was one of the fastest growing skills on LinkedIn and recorded a 190% global increase in demand between 2015-1017.

Machine Learning is evolving quite rapidly and gradually nowadays. Therefore, A lot of technology professionals are required in the coming years in the area of Machine Learning.

ML has sound career choice for a while now because of the growing adoption of the technology across industries. Because this is an emerging technology, and it’s pulling in people from all sorts of disciplines, we don’t really have a great precedent for it yet.

Having a Computer Science background is very important to have a rewarding career in Machine Learning.

Career Path

Machine learning is a valuable technology in any industry that works with large amounts of data. Today, machine learning and AI technology have become integral in many of the leading professional industries. New technology is constantly being adapted to help businesses stay competitive in a landscape that’s fueled by advancing technology. For example, we can look at the healthcare industry and how the use of wearable devices has enabled providers to elevate their level of care by accessing patient data in real time, or how the financial sector is leveraging machine technology to gain more profound insights into data and level up their fraud protection capabilities. Every industry, from the government to marketing is learning how to adopt machine learning technology best to more effectively streamline and enhance their operating processes and level of customer satisfaction.

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Top 5 Benefits of Gaining Machine Learning Skills

Simplifies Product Marketing and Assists in Accurate Sales Forecasts

ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to sales and marketing sector, with the major ones being –

  • Massive Data Consumption from Unlimited Sources

    ML virtually consumes unlimited amount of comprehensive data. The consumed data can then be used to constantly review and modify your sales and marketing strategies based on the customer behavioral patterns. Once your model is trained, it will be able to identify highly relevant variables. Consequently, you will be able to get focused data feeds by foregoing long and complicated integrations.

  • Rapid Analysis Prediction and Processing

    The rate at which ML consumes data and identifies relevant data makes it possible for you to take appropriate actions at the right time. For instance, ML will optimize the best subsequent offer for your customer. Consequently, the customer will be able to see the right offer at a given point of time, without you actually investing time to plan and make the right ad visible for your customers.

Easy Spam Detection

Spam detection was one of the earliest problems solved by ML. Few years ago email providers made use of rule-based techniques to filter out spam. However, with the advent of ML, spam filters are making new rules using brain-like neural networks to eliminate spam mails. So, The neural networks recognize phishing messages and junk mail by evaluating the rules across a huge network of computers.

Simplifies Time-Intensive Documentation in Data Entry

Data duplication and inaccuracy are the major issues confronted by organizations wanting to automate their data entry process. Meanwhile, this situation can be significantly improved by predictive modeling and machine learning algorithms. With this, machines can perform time-intensive data entry tasks, leaving your skilled resources free to focus on other value-adding duties.

Interpret Past Customer Behaviors

ML will let you analyze the data related to past behaviors or outcomes and interpret them. Therefore, based on the new and different data you will be able make better predictions of customer behaviors.(Source-

Exceptional Career Opportunities

It can feel like a gamble to invest time and money into learning a new skill and then keep your fingers crossed that you’ll find your way into the professional position you deserve. This isn’t the case with machine learning.

Prerequisite Before Learning ML

1. Computer Science Fundamentals

Fundamentals of ML is Computer Science include data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity.

2. Probability and Statistics

A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.). Techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many Machine Learning algorithms.

3. Data Modeling and Evaluation

Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigen vectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.).

4. Software Engineering and System Design

At the end of the day, a Machine Learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services.

Jobs in ML

Although we talk about AI and machine learning as broad categories, the jobs available are more specific. Some of the jobs described by Van Loon during the webinar include:

Machine learning researcher
AI engineer
Data mining and analysis
Machine learning engineer
Data scientist
Business intelligence (BI) developer


The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems. If this is YOUR future, then there’s no time like the present to start mastering the skills. So that developing the mindset you’re going to need to succeed.