Elevate Your CV: How Learning Machine Learning Career Boosts Your Profile

With 82% of companies requiring Machine learning engineers, choosing it as a career choice is among the wise decisions. Regardless of one’s previous study and job background, career transition in the field has brought forward successful results. Working hard to gain proficiency in skills and gaining hands-on experience on projects is the gateway to opportunity in the field. Moreover, coupling it with a well-polished resume strengthens your chance of clearing the first stage of selection, followed by a test or interview where candidates are expected to exhibit their possession of knowledge and experience.

Learn in detail about the potential of a machine learning career, the necessary skills, the roadmap and more.

Here’s what we’ll cover in this article:

• Is Machine Learning a Good Career?
• What Skills are Necessary for a Career in Artificial Intelligence and Machine Learning?
• How to Start a Career in Machine Learning: Complete Career Path
• Tips for Creating an Impactful Machine Learning Profile
• FAQs about Machine Learning Career

Is Machine Learning a Good Career?

What do Experts Say?

“Machine intelligence is the last invention that humanity will ever need to make. With data collection, 'the sooner, the better' is always the best answer.”

~ Marissa Mayer
Former President and CEO at Yahoo

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Careers in computer-based courses, with a focus on Machine Learning, offer a promising option to aspirants. With a global surge in demand for AI-driven solutions, ML careers offer room to exhibit skills and innovate. Machine Learning presents a myriad of opportunities because of the following:

• Increasing demand for machine learning professionals owing to the increase in AI incorporation into business
• Promising salaries and stable career options
• Contribution to different sectors as per one’s expertise and interest
• Machine learning is capable of bringing and has brought a revolution in industries. The positive impact is evident in society, such as the manufacturing of medical devices, self-driving cars and others
• Candidates can expect options to choose from among numerous career paths, such as NLP scientist, Business Intelligence Developer, Data scientist, Machine Learning Engineer and others.
• Job opportunities can be both online and offline depending upon the company and type of work, thus offering flexibility in working schedule.

What Skills are Necessary for a Career in Artificial Intelligence and Machine Learning?

The important skills for Machine Learning are as follows.

Technical Skills

• Knowledge of programming languages like R, Python, Java, SQL and C++ with statistics, probability, physics and mathematics.
• Ability to design systems, understand and create reliable features, solutions and software
• Skilled in data modeling and evaluation, neural networks, natural language  and audio and video processing
• Should know about advanced signal processing techniques and reinforcement learning
• Familiarity with machine learning libraries and frameworks such as PyTorch, scikit-learn, TensorFLowand multiple others.

Non-technical Skills

• Ability to efficiently collaborate and explain to the juniors, team members, seniors and officials from the non-technical background
• Can overcome novel challenges with novel solutions with critical and logical thinking
• Multitask with proper time management
• Domain knowledge, rapid prototyping and will upgrade the skills

Machine Learning Careers and Job Outlook

As stated previously, Machine Learning encompasses wide scope for the aspirants. The job outlook has been promising from top experts in the industry. The World Economic Forum’s Future of Jobs Report 2023 states that the Demand for AI and Machine Learning Specialists is expected to grow by 40% or 1 million jobs. Interested candidates can choose from the following famous career paths:

• Machine Learning Engineer: Handle Machine Learning projects
• Software Engineer: Work on software solutions through research, curation and implementation
• Business Intelligence Developer: Leverage the potential of data for rational business-based decisions concerning the objectives and goals
• Computer Vision Engineer: Work on visual data
• Data Scientist: Perform data collection, analysis and interpretation of complex datasets
• Natural Language Processing: For understanding and contextualizing human language
• Human-centered Machine learning Designer: Work on information systems for human collaboration while prioritizing user needs, ethics and experience

How to Start a Career in Machine Learning: Complete Career Path

Thinking about beginning a career in Machine learning from any background through domain change is the common approach owing to the AI revolution. With people switching to the technical background, the most important thing is the will to learn and keep upgrading with technological advancements. Currently, educational platforms offer hybrid learning models at one’s preferable time to aid in switching careers. The path to machine learning success can be summarized as follows:

• Pursue a Degree in Computer or Mathematics

Basic clarity in concepts of computers, programming languages and mathematics is one of the basic needs to become a machine learning engineer. A Bachelor’s degree acts as a stepping stone. However, career transition through introductory courses is also possible.

• Learn programming languages

Programming language is the base of work in software and machine learning engineering. The ideal candidate is expected to possess excellent familiarity with a good portfolio of languages like Python, R, C++ or Java.  Working knowledge or proficiency to deal with real-life challenges is one of the key skills desired by the companies.

• Learn Concepts and Tools

Understanding of concepts like supervised, unsupervised and deep learning. Model evaluation, bias and fairness and similar others find daily usage in machine learning jobs. Similarly, tools like Jupyter Notebooks, TensorFlow, PyTorch, scikit-learn, Keras and Pandas are equally popular requirements.

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• Gain experience

Look for experience through internships, entry-level jobs or collaboration under projects. Get familiar with skills, programming languages, concepts and tools through  real-world projects. Learn about the functionality of projects and the tasks performed at the basic level. Use the same to design projects yourself.

• Enroll in Certification Courses

With concept clarity of basics, candidates can opt for certification courses from software developer companies like Microsoft or Google. Clear the exam and apply for the job requiring proficiency in the tools you opted for. It enhances the chances of landing the job owing to certified proof of the presence of required skills and knowledge.

• Build Network

Increase connections with people from the field through interactions via workshops, conferences and events. Connect with professionals and look for opportunities to work under them or simply assist them in their projects. Networking offers excellent opportunities to get along with more other people in the field who can offer better possibilities for a career based on one’s skills. Additionally, you can participate in contests to exhibit your skills.

Tips for Creating an Impactful Machine Learning Profile

Develop something unique in yourself and state the same to the recruiter. Besides, here are some key techniques for an impactful machine learning profile:

• Build a portfolio and put the projects to showcase your abilities, skills and will to learn
• Display the contribution to projects and team works, preferably through statistics and numbers, by focusing on efficiency gains, revenue impact and user engagement
• Emphasize communication and problem-solving skills and back them with proof
• To land a job, don't rely only on the resume. Know your strengths and pen them down in a personalized cover letter too.
• Tailor your resume based on the job description rather than sending the generalized one. Include keywords from the job description to make your profile ATS compatible.
• Highlight your knowledge of current trends and industrial scenario
• Add relevant volunteering too as it speaks for your practical skills and dedication to the field.

Consider a well-polished machine learning resume below for your reference.

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FAQs about Machine Learning Career

Q1.What do I have to know to start a career in machine learning?

Understand your knowledge about computers, programming languages, mathematics and currently trending tools. Build your knowledge base and application skills to start a career in machine learning.

Q2. How to change your career to machine learning?

Begin with analyzing your skillset and accordingly gaining knowledge and experience in foundational requirements of Machine Learning. Follow it up with certified courses and internships to gain experience.

Q3. How long does it take to become a machine learning engineer?

The time varies based on one’s educational background, time invested in learning and ability to learn. An online course offering a brush-up on basics and interview preparation can prepare a candidate familiar with concepts in 2 to 4 months through Interview Kickstart.

Q4. What is the salary of a beginner machine learning engineer?

The average salary for a machine learning engineer is $1,51,948 per year in the United States. Candidates can expect an average additional cash compensation option of $29,336.

Q5. Does machine learning require coding?

Coding is one of the fundamental requirements for machine learning. One can easily gain familiarity with programming languages through online courses.

Ace Your Next Machine Learning Interview with IK

Top companies looking for Machine learning engineers and related positions can be your next stop if you aim for them. Helping you through the preparatory stage, Interview Kickstart aims to guide and help you revise every crucial detail pertaining to machine learning. Assisting in the journey, our promising expert-designed mock interviews and learning modules have placed numerous students in brands like Amazon, Google, Microsoft and many more. Begin your journey to becoming a Machine Learning engineer now!

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