Machine learning has been touted as a revolutionary technology by most industry experts, one that has been spearheading Industry 4.0. As the demand for this technology continues to rise, starting a career in the field of machine learning field or transitioning to machine learning jobs can be challenging. As an aspiring individual, trying to establish yourself in the industry, you must keep updating yourself, bettering your understanding of the subject, and picking up new skills along the way. However, the key to being successful in your machine learning career path lies in identifying common mistakes that most machine learning enthusiasts make at different stages of their career, and avoiding them. Let’s take a look at some of the common mistakes and learn how we can avoid them. 

Common Mistakes in the Machine Learning Career Path

From entry-level to seasoned professionals, each individual trying to move ahead in their machine learning career path is bound to face different hurdles. For instance, if you are a fresh graduate, you need to understand the difference between theory and actual applicable concepts. This gap in comprehension can often lead to unsuccessful interviews for machine learning jobs. Similarly, experienced professionals making a career transition often make the mistake of taking a break from learning new things – in an industry that is continuously evolving, this can be extremely harmful to one’s career. 

For ease of understanding, we have classified the common mistakes into two categories, according to one’s professional life cycle –

  • Mistakes to be avoided by entry-level professionals
  • Mistakes to be avoided by seasoned or experienced professionals

Mistakes to be Avoided by Entry-level Professionals in Machine Learning Career Path

As someone new to the industry, you are bound to make a certain set of mistakes. Let’s identify the common mistakes and try to find some solutions. 

  • Focusing only on tools and libraries: New entrants often tend to focus more on tools and libraries. They often neglect practical know-how and the understanding of business problems posed by the domain. For example, TensorFlow is a well-known library which helps in training the machine to understand data. It supports various data formats such as images, audio files and text. While it is a very helpful resource, many beginners often learn this tool only because TensorFlow is famous. Ideally, at the entry level, you should try to learn and understand how each tool addresses business problems.

    How to avoid this problem: When you apply for a particular machine learning job, it is important that you read up on how companies in that particular domain are using machine learning to improve their business processes. Read more about the top machine learning companies in the world here. Interacting with peers or experienced individuals in the field of machine learning can also help you understand the fundamentals more clearly, and help you map your knowledge of programming and tools to break down the problem. This will not only help you discuss ideas, but also give you a way to showcase your understanding of the subject. Tools and libraries do serve an important purpose but your focus should be on understanding the fundamentals first.
  • Not focusing on the basics: After graduation, many aspirants usually don’t revisit the basics. As an entry-level professional, your primary focus should always be on getting the basics right. From programming languages to logical problem-solving skills, an interviewer is bound to assess your basic skills. Furthermore, many entry-level professionals end up focusing more on advanced concepts without building a solid foundation, and this is a mistake that you should definitely avoid.

    How to avoid: The best way to avoid this mistake is to focus on the fundamentals first. When you have a proper grasp of the basic concepts, advanced problems will be easier to understand. One subject that is often overlooked is mathematics; avoid that. Practice it regularly to find the best ways to describe and solve a problem. Sites like HackerEarth and MachineLearningMastery often provide articles and resources that can help you learn and revise your basics.
  • Drifting away from algorithms: Algorithms are like black boxes for machine learning professionals. As a beginner, it is important to learn and understand the thought process behind algorithms before using them. As a machine learning engineer, you would be expected to understand the root cause of a problem and find a way to solve it. Therefore, it is important to dive deep and learn the reasoning behind the use of certain algorithms. Also, many reputed machine learning companies focus on evaluating candidates on the basis of their knowledge of algorithms. 

    How to avoid: Simply put, focus on understanding the logic behind algorithms. By doing so, you will develop a systematic approach towards tackling a problem in your machine learning career. Brush up on your knowledge of algorithms before interviews – it will help you showcase your understanding and crack the interviews for machine learning engineer jobs.  
  • Sticking only to projects on the internet: Many new aspirants often end up working only on projects that are commonly available on the internet. Along with being easily available, they don’t add much value to your resume as they are repeated by many other aspirants too. Projects that are mentioned in your resume are supposed to showcase your skills and talent for understanding the concepts of machine learning. By working on generic projects from the internet, you end up limiting yourself to easily accessible solutions. Since the solution and the approach taken by you will end up being similar to that of the others, many employers will not be interested in considering your profile for a role.

    How to avoid: Work on projects that challenge you – you will be able to understand the concepts learnt in depth. Along with this, a new project will encourage you to think outside the box and come up with innovative solutions and take a modern approach to tackle problems. This is what appeals to a lot of interviewers and companies during recruitment drives. 

Mistakes to be Avoided by Experienced Professionals in ML Career Path

Similar to entry-level professionals, seasoned professionals and those looking for a career transition also end up making a few mistakes. Let’s take a look at some mistakes and possible solutions.

  • Implementing machine learning algorithms without a valid reason: Machine learning jobs such as machine learning engineer jobs require you to work on many algorithms. Each one of them is complex and requires a lot of steps and processes to be followed while coding and during execution. However, if you focus more on implementing algorithms without understanding the overall problem, you could end up increasing the overall cost of the project by using up time and manpower. For instance, the algorithms used to solve business problems are not simple; they require a lot of resources to be employed. In such scenarios, it is important to analyse the situation before you start working. As a machine learning engineer, critical thinking and decision-making skills are extremely essential. Therefore, implementing an algorithm without visualising the process can end up complicating things for your entire team.

    How to avoid this: Understanding the problem statement is cardinal as it. It will help in creating a proper strategy to solve the problem, and professionals can easily implement a simpler route than using a complex algorithm. Also, as an experienced professional with a thorough understanding of concepts, you will be able to come up with alternative ideas that will help achieve similar results. Many industry professionals often emphasise on visualising the model before working on it. One of the most suggested methods is to try using a GUI based framework such as Weka or Orange to understand this process.
  • Overestimating the value of educational qualifications: A sound educational background is necessary to improve your chances of getting selected for machine learning engineer jobs but sometimes, professionals can end up overestimating the value of their degrees and diplomas. While it may be an important factor, it is not the only one. In a world where technology is evolving at lightning speed, you must build on your academic knowledge and keep updating yourself along with your industry. 

    How to avoid this: If you are considering transitioning into a machine learning career path, supplement your coursework with plenty of projects that address real-time problems. This will add value to your resume and give you a proper understanding of how things actually work in the industry. Additionally, interact with people from the industry who can mentor you and help you understand the work that goes in for a project or process in a team. 
  • Stopping yourself from learning something new: One of the biggest mistakes you can make is holding back when it comes to learning something new. Staying up to date with new technologies is an absolute necessity and something that requires constant attention.

    How to avoid this: As machine learning is evolving at a fast pace, it is imperative that you try to learn new things on the go. From participating in advanced programs to attending webinars and seminars from sites like DataRobot and Kaggle, engage your critical thinking faculties in innovating for your industry. 
  • Efficiency over effectiveness: Every seasoned professional seems to be focused on the accuracy of a model rather than understanding how it works. While the solution may be delivered, its effectiveness may not be clear. As the focus is always on working towards a solution, the efficacy of a model often gets overlooked in a machine learning workflow.

    How to avoid this: The way to avoid this mistake is simple – prioritise efficiency and process. As an aspiring machine learning engineer, learning about a model and its operations will help you inform the various parties involved in a detailed way. When you spend time analysing and understanding the problem statement, you improve the overall process by reducing the time and complexity involved.

By identifying and avoiding the common mistakes mentioned above, you can stay strong on your machine learning career path. Make sure to keep yourself updated and stay relevant. Wondering how you can do it? Springboard’s machine learning career track program is a 1:1 mentoring-led and project-based online program that will help you prepare for machine learning jobs. Check out our online program to know more about it.