Machine learning has formed its impetus in today’s world. The field has found its application in numerous forms paving way for endless opportunities.
Machine learning is one of the most popular technologies in the current automation market because of its capabilities to learn, adapt, and perform tasks. Teaching a computer system how to work and perform by feeding it specific data helps a great deal with making predictions and uncovering hidden patterns. Making a machine intelligent enough to carry out an activity that would originally be performed by human intelligence is just one aspect of AI, but it is also one of the most intriguing factors of the AI domains and subdomains. In fact, according to an article by G2, 65% of companies planning to adopt machine learning say the technology helps businesses in decision-making.
Now, as the adoption and the desire of ML deployment increases with each passing day, there has also been noticed a surge in the demand of professionals in the ML arena.The same article by G2 also states that More than 98,000 jobs posted on LinkedIn list ML as a required skill, along with Machine learning, NLP and deep learning being the three most in-demand skills on Monster.com.
It is clear that there is a dire need of industry specialists in the ML sector, but what exactly is it that one will need to enter the industry with an ML job?
Well, ML is a highly intricate and specific sector that has various roles available for job seekers. Some of the Machine Learning Roles are –
- Data Scientist
- Cybersecurity Analyst
- Cybersecurity Automation Specialist
- ML Developer
- Applied Scientist
- ML Engineers
- ML Cloud Architect
- AI/ML Researcher
- Robotics Engineer
- ML Technical Analyst
- Data Analyst
- Computer Vision Engineer
- Deep Learning Scientist
Since Machine Learning sits at the top of the tech hierarchy in the job market, it also requires some specific skills and expertise to enter the industry. Some of the much needed non-technical skills for ML jobs are –
- Communication skills
- Team work abilities
- Organized
- Project management
- Leadership skills
- Data-driven decision making
- Cognitive thinking
- Analytical reasoning
And some of the much needed technical skills would be –
- Software Architecture
- Software Development
- Data Infrastructure
- SQL
- Python
- R
- Java
- Data and Dataset Management
- Data Science
- Data Annotation
- Hadoop
- Statistical Analytics
- Algorithmic Model Development
- NLP
- Neural Networks
- Deep Learning
- Pattern Detection
- Data Analysis
- Data Visualization
- Applied Data Science
Now, the pining question that has everybody’s attention is that how exactly should you get started in ML?
ML is integrated into various industries with multiple distinct roles and functions, so the first thing for you to do would be identifying and understanding your area of interest.
So, for example, if you are someone who would rather train the ML model, than build it, then the best choice for you would be getting into the training specialization. And if healthcare is an industry that highly motivates you, you can learn healthtech specific machine learning model training.
Next, you would want to get a degree in computers with specialization in ML, or you can learn from various resources available on the internet and take up courses offered by machine learning industry leaders to fully equip yourself with the required knowledge and qualifications for a job in ML.
One thing that helps freshers get in is going through various job roles in advance to see the market trends and requirements and then acquire those specific skills and knowledge to increase the chances of getting hired.
Getting professional certification courses done and even attending hackathons and webinars pumps up your ML data skills and paves your way to getting the best fit ML job of your dreams!
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