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Have you heard the following saying from Albert Einstein?
Insanity is doing the same thing over and over again and expecting different results.
It's a perfect reminder for those starting their ai journey. As a beginner, it's easy to feel overwhelmed by the wealth of information and resources available. You may make the same mistakes that many others have made before you. But why waste time and energy repeating those mistakes when you can learn from their experiences?
As someone who has spoken to seasoned professionals in the field, I've always been curious about their ai journey. I quickly discovered that many of them faced similar challenges and difficulties from the beginning. That's why I'm writing this article: to share the five most common mistakes ai beginners often make, so you can avoid them.
Then let's get started:
1. Overlooking the fundamentals
As a beginner in ai, it's easy to get excited about flashy algorithms and powerful frameworks. However, just as a tree needs strong roots to grow, your understanding of ai needs a solid foundation. Ignoring the math behind these basic components can hold you back. Frameworks exist to help the computer perform calculations, but it is important to learn the underlying concepts rather than simply relying on black-box libraries and frameworks. Many beginners start with tools like scikit-learn, and while they can get results, they often have difficulty analyzing performance or explaining their findings. This usually happens because they skip the theory. To become a successful ai developer, it is essential to learn these basics.
Determining what skill sets separate a good ai developer from a novice is not a simple, one-size-fits-all answer. It is a mixture of several factors. However, for the purposes of this discussion of fundamentals, it is important to emphasize the importance of problem solving, data structures, and algorithms. Most ML companies will assess these skills during the hiring process and mastering them will make you a stronger candidate.
2. The fallacy of the jack of all trades
You may have seen profiles on LinkedIn claiming to have expertise in ai, ML, DL, CV, NLP, and more. It's like a long list of skills that can make your head spin. Maybe it's social media or the “full-stack developer” trend that people compare ai to. But let's be real: living in a fantasy world won't help. ai is a very broad field. It is unrealistic to know everything and trying to do so can lead to frustration and burnout. Think of it this way: It's like trying to eat an entire pizza in one bite; Not exactly practical, right? Instead, focus on getting really good at a specific area. By narrowing your focus and devoting your time to mastering one part of ai, you will be able to make a significant impact and stand out in the competitive world of ai. So, let's avoid spreading ourselves too thin and focus on becoming an expert in one thing at a time.
3. Stuck in the tutorial trap
I think the biggest mistake beginners often make is getting overwhelmed by the countless online tutorials, courses, books, and articles available when learning ai. Learning and participating in these courses is not a negative thing. However, my concern is that they may not find the right balance between theory and practice. Spending too much time on tutorials without actually applying what you learn can lead to a frustrating situation known as “tutorial hell.” To avoid this, it is important to test your knowledge by working on real-world projects, testing different data sets, and continually working to improve your results. Additionally, you will notice that some concepts taught in courses may not always work best for specific data sets or problems. For example, I recently saw a session on Align LLMs with DeepLearning.ai Direct Preference Optimizationwhere the research scientist ED Beeching from Huggingface mentioned that although the original direct preference optimization paper used RMSProp as the optimizer, they found Adam to be more effective in their experiments. You can only learn these things by gaining practical experience and immersing yourself in practical work.
4. Quantity over quality projects
When beginners want to show off their ai skills, they are often tempted to create numerous projects to demonstrate their expertise. However, it is essential to prioritize quality over quantity. I've noticed that people who work at large tech companies often have 2-3 solid projects on their resumes, rather than 6-10 small or mediocre projects that include many others. This approach is not only beneficial for job prospects but also for learning. You can get a better understanding of the topic. Instead of following YouTube tutorials or creating a bunch of average projects, consider investing a month or so of your time and energy into projects that will have long-term value. This approach will intensify your learning curve and really highlight your understanding. It can also make your resume stand out from others. Even after getting a job, you won't have much trouble transitioning to the real job.
5. Lone wolf syndrome
I understand that different people have different work preferences. Some may prefer to work alone, while others seek support. For beginners in machine learning, it can be overwhelming and working in isolation can hinder your growth. I highly recommend engaging with ai communities on platforms like Reddit, Discord, Slack, LinkedIn, and facebook. If you're not comfortable with communities, consider finding an ai mentor to provide guidance and support. Discuss your projects with them, seek their advice and learn better approaches. This not only makes the learning process enjoyable but also saves time. Although I don't recommend that you immediately post questions or contact your mentor as soon as you encounter a problem, you should always try to solve it yourself first. But after a certain point, it's okay to seek help. This approach saves you from burnout, improves your learning, and in the end you will feel good about yourself for trying and gaining insight into what didn't work.
50-day challenge: dare to accept and improve your ai skills
Throughout this article, we have discussed the 5 most common mistakes that beginners should avoid at all costs.
I have a EXCITING CHALLENGE for all of you. As a responsible member of this community, I want to encourage you to take action and apply these tips on your own ai journey. Here is the “50 Day Challenge”:
1. Write “Challenge Accepted” in the comments section below. (Please reload the page if you can't see the comments section; it may take some time for it to appear.)
2. Spend the next 50 days focusing on these 5 tips and implementing them into your ai learning.
3. After 50 days, come back to this article and share your experiences in the comments. Tell us what changes these tips brought to your life and how they helped you grow as an ai practitioner.
I look forward to hearing your stories and learning about your progress. Also, if you have any additional suggestions or tips for other readers, please share them! Let's help each other grow.
Kanwal Mehreen Kanwal is a machine learning engineer and technical writer with a deep passion for data science and the intersection of ai with medicine. She is the co-author of the eBook “Maximize Productivity with ChatGPT.” As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She is also recognized as a Teradata Diversity in tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is a passionate advocate for change and founded FEMCodes to empower women in STEM fields.
