Is 30 Too Old to Learn Machine Learning?

Contrary to popular belief, your 30s is the perfect time to dive into something new, especially if that something is a rapidly growing field like machine learning. As you enter adulthood, there is often a misconception that the things you learn must be built off of things you’ve picked up in previous years, but that simply isn’t true. The older and more experienced you are, the more in tune you become with your education and interests. That means you can approach new challenges with confidence and wisdom that the younger you might not have had. Picking up machine learning in your 30s not only aligns with the evolving demands of the job market but also makes use of your maturity and experience. This article walks you through learning machine learning in your 30s, and why even at this age it is an excellent investment of your time and resources.

Why Learn Machine Learning at 30?

The last decade has seen a recent boom in artificial intelligence. Machine learning falls under this umbrella, and refers to the use of algorithms to create systems that machines can use to learn from themselves, make decisions, or fulfill other tasks without human help. Though learning a skill like this at any age would be valuable, there are particular reasons why beginning your journey in your later adulthood might be particularly important, starting with your level of professional experience. By your 30s, you likely have substantial professional experience that can be used as a foundation or a springboard into a new field. Whether that experience is directly related to technology does not matter. Machine learning is used in a diverse range of fields, including education, finance, and healthcare. Having that work experience in your back pocket can allow you to immediately see practical professional applications that a younger you might not have. Because machine learning is a highly sought after skill, it’s great for building on an established professional profile, portfolio, or resume. 

There is another compelling reason to learn machine learning later on in life: career advancement. Networking opportunities provide significant benefits for working professionals. In your 30s, you may have a strong professional network that can be highly advantageous when applying for jobs, changing careers, or advancing yourself in your industry. Leveraging new connections, especially in a field like artificial intelligence, can help amplify the work you already do. It can open doors to more collaborative projects and mentorship opportunities and can be a great help when shifting careers. If you are not making a big change, bringing machine learning into your work can give you access to additional markets, or can make you a valuable asset to your company. These skills apply to a broad job market, so no matter when you learn machine learning, it can benefit you professionally. 

Machine learning also comes with an abundance of available resources. As artificial intelligence becomes more entwined with our daily lives, machine learning continues to evolve. Courses, tutorials, community forums, and study groups become a great way to make new and like-minded friends in your 30s. Most courses are accommodating to a wide range of ages. Constantly learning new skills is a testament to your commitment when it comes to personal and professional growth, and that can make a huge difference in terms of promoting yourself in new work opportunities.

How Long Will it Take to Learn Machine Learning?

The time it takes to learn machine learning varies by person and is impacted by several factors, including your background with the topic, the amount of time you have to study, and the depth of knowledge that you want to achieve. If you have a solid foundation in a directly related topic like programming, math, or technology, grasping a solid foundation in machine learning basics can be done within a few weeks to a few months, and you can do things like apply basic machine learning algorithms, or build simple models. Beginner skills also include things like data processing and visualization, and model evaluation. Using an online course, tutorial, workshop, bootcamp, or other learning platform can further accelerate this process. With a targeted tutorial, picking up the foundations can happen in as little as a few weeks. However, the time commitment and consistency in studying still play a role in determining how quickly you progress. 

Achieving a more professional skill set in machine learning will take time. Getting to a proficiency where you can confidently design and implement more sophisticated models, algorithms, and concepts may take up to a year or more. Machine learning is constantly evolving, so even the skills that you learn one year may not be as up-to-date or relevant in the coming years, which can require additional training. Current intermediate and advanced topics include natural language processing, deep learning, neural networks, reinforcement learning, and big data. Studying in an immersive environment with access to professionals, peers, or community members who can help, will have an additional impact on how long it takes to acquire advanced topics. Engaging in hands-on projects and real-world applications can also significantly enhance your understanding and help you pick up machine learning more quickly. 

Learning machine learning in your 30s offers unique advantages and disadvantages which can impact how quickly you learn. You likely have a lot of professional experience which can help you practically apply your machine-learning skills very quickly. You’re also likely more familiar with your preferred learning style and overall goals, which will make it easier to take courses that are specific to your needs and interests. However, at this age, you are also more likely to have obligations that might interrupt your speed of learning. This includes family, work, or other school obligations. A realistic study schedule may not allow you to take full-time classes, in which case part-time classes, workshops, or short courses may be ideal. With the help of these course types, you are still able to learn machine learning in a structured way and can pick up the basics in as little as a few months. Getting to a professional or advanced level skill at a part-time workload might take longer than a year, but is still worth pursuing if you want to add a relevant and growing skill to your repertoire. 

Ways to Make Learning Machine Learning Easier and Quicker

One of the most effective ways to make machine learning quicker is to start with what you already know. For those who are into technology, you’re likely familiar with Python, or R. Being proficient in either of these languages will allow you to skip the programming courses and jump straight into machine learning concepts. Additionally, understanding math principles like algebra, calculus, or statistics will make grasping machine-learning concepts more intuitive. Though these aren’t required prerequisites, they can be useful in cutting down on the time it takes you to get to things like algorithms and data handling. 

Another thing to consider is choosing the right learning resources for you. This takes into consideration things like your preferred learning style, the proficiency level, the learning schedule, and the course type. If you best learn through in-person instruction, which can be great in terms of feedback and building community, finding a nearby college or institute is highly useful. If you like creating your schedule or have obligations that might keep you from a strict schedule, there are on-demand courses that will better suit your needs. These are offered online and taught through things like videos, modules, and interactive websites. If you do enjoy speaking with a real, live instructor or industry professional, but still want flexibility in terms of where you learn, live online is somewhat of a middleman. It is offered online, but there is a learnins schedule. A subject like machine learning is regularly evolving, so live classes are highly recommended. They allow you to immediately ask questions from those in the field, and access some of the world’s leading facilities, limiting the amount you need to pay upfront to get software, machinery, or other required tools. 

Learning machine learning full-time is certainly a recommendation for those who want to learn machine learning quickly. There are also many direct advantages to full-time. Bootcamps and intensive courses are designed to develop skills rapidly and often boast a structured curriculum that will walk you through the concepts, techniques, and requirements in an intuitive way. These programs are demanding and can be overwhelming for some, in which case it’s important to know your boundaries and limitations to avoid burnout. With this in mind, full-time is the best option for those who are prepared for the workload and want an accelerated track. Part-time options are much more flexible and give you more breathing room, which can make them easier. They still have a structured curriculum and access to resources and industry professionals, but the schedule is more manageable, and the content can be more digestible. 

If you’re here to learn more about how you can leverage your 30s to make machine learning easier and quicker, then the biggest piece of advice is to use your network and your previous experience. Leveraging the people and things you know can be extremely valuable in growing your skills. When you take live classes, both of these are directly impacted. You will shake more hands, make more friends, and have an opportunity to ask questions of people who have been in the field of machine learning, and who can loop you in on shortcuts and best practices. Ultimately, these connections are what turn into unique job opportunities later on down the line. It’s easy to understate how important peer and mentor connections are in maintaining motivation, but it’s easier to overcome challenges when you have support. You can significantly enhance your machine learning journey at any age, and smoothly learn more about artificial intelligence with the help of these recommendations.