Is Machine Learning a Hard Class?

Machine learning is a highly technical topic that can be a struggle for some, but not all. The level of difficulty depends heavily on a few factors, including your prior knowledge of closely related topics like programming, math, or data analysis. Those with a strong background in any of the foundational skills might have an easier time transitioning to topics specific to machine learning, including algorithms, neural networks, or model evaluation. There is also the matter of how much time you can reasonably dedicate to picking up the subject. Those learning will benefit from a lot of hands-on instruction, and project-based opportunities, which can take time and money. 

Machine learning is still a skill that anyone can pick up with the right materials and support. Choosing resources that are appropriate for your proficiency level will become a vital part of making sure that you learn machine learning efficiently. Finding a learning path that matches who you are as a student will make the overall process more manageable, and while artificial intelligence is constantly evolving, which can further complicate machine learning resources or content, this is still a highly achievable skill for those who are fully dedicated and prepared to start their journey.

What is Hard about Learning Machine Learning?

Machine learning can be difficult because of the foundational knowledge required to be successful. As a branch of artificial intelligence, a basic understanding of technology, programming, and math are almost expected. Though this can come intuitively for those who have experience, learning core concepts in these related fields is not as simple for everyone. There is a sharper learning curve for those without prior experience in STEM. There is also the complexity of some of the content. Algorithms are taught throughout the course, but those who are working with them for the first time may need additional assistance to fully understand the relationship between algorithms and how they lead to machine learning. This becomes especially true at the intermediate or advanced levels, where topics like neural networks, deep learning, or natural language processing are prominent. Learning how to work with abstract concepts in machine learning can be extremely difficult.

There are also occasional challenges with turning knowledge from theory to practice. Practical application of machine learning concepts requires specific tools and can often benefit from hands-on experience and regular experimentation. Implementing machine learning models in everyday tools requires confidence and an understanding of data preprocessing, or taking raw data and making it suitable for training machine learning. Debugging and ensuring that models are doing what they should is a large part of intermediate and advanced work, and fully absorbing these skills requires great patience and flexibility. As you go through your machine-learning journey, you will also learn how to pick appropriate algorithms and search for errors in your machine-learning systems so that you can problem-solve in real-time, and use those skills in your professional career later on down the line. Certain techniques require a combination of both theoretical and practical skills. Techniques like reinforcement learning have been flagged as particularly tricky to learn for this reason. 

In terms of roadblocks to look out for, your proficiency level will dictate which things might be more difficult for you. At the beginner level, that may look more like conceptual knowledge, like learning the difference between supervised and unsupervised learning, or learning which data is important and usable in machine learning. At the intermediate level, the challenges shift toward choosing the best algorithm or evaluating your models. These may seem like beginner concepts, but many times, algorithms are provided for you as part of a beginner course, and choosing or working with new algorithms is a transition to the higher levels. As you move into professional-level or advanced skills, the roadblocks become more about highly complex information and concepts like deep learning, which refers to the algorithms in which minimal to no human help is required. Machine learning uses thousands of data points to teach the machine, whereas deep learning can use upwards of millions of data points. Every skill level presents unique challenges that may become full roadblocks without the right support, resources, or motivation. 

How Can I Make My Machine Learning Training Easier?

The easiest way to learn machine learning will differ by person. As we all have unique learning styles that dictate which classes, schedules, and resources work best for us, this section will discuss general recommendations that can work for anyone. This starts with free resources. These come in a wide range of formats. From online tutorials and courses to open-source libraries and other documents, free resources are a great starting point and can make machine learning easier. Platforms like Coursera, edX, and Khan Academy offer free courses on machine learning that can be used to familiarize you with core concepts. High-quality tutorials and lecture notes can be accessed through university websites or YouTube. Open-source libraries like PyTorch and Scikit-learn are great tools for hands-on practice with community support available to help you along the way. There are additional sites that host open-source projects that you can study from or contribute to. This provides practical experience that will help build confidence over time, and that will make learning easier in the long run.

Live courses can also assist in making machine learning content more digestible. These courses are offered both in-person and online. When you attend a class at a university, college, or training institute, it comes with a host of benefits including a highly interactive and collaborative learning environment, immediate and consistent feedback, and plenty of hands-on practice. In-person classes aren’t known for being flexible schedule-wise, in which case a virtual option may be more comfortable. You will still have the opportunity to ask questions in real-time, but you won’t be face-to-face with your instructor or your peers, which can impact motivation for some people. Even though live courses are highly recommended, not all students have availability that allows for a course with a strict schedule, in which case On-Demand classes should be considered. These courses are taken at the student's discretion. Once the fee is paid, courses can be accessed anywhere, anytime. All three course types will offer a structured curriculum and appropriate materials. Though live courses can make machine learning easier just from an interaction and feedback perspective, that means very little if a student does not have time available to fully invest in those classes, in which case On-Demand classes are a viable replacement. 

Bootcamps are intensive courses that can cover skills of different proficiency levels in a cohesive and manageable way. Though they are longer, have a heavier schedule, and are structured in a way that can teach you a lot in a little bit of time, they are a great short-term learning experience. If you want a learning experience that is tailored towards job-focused skills, a bootcamp can help. This can make machine learning skills easier in terms of transitioning into your future career and professional opportunities. There are also highly valuable networking opportunities available through a course like this, which can connect you with industry professionals and potential employers. Building a supportive learning community can make all the difference in how easily you learn machine learning. This is not only limited to bootcamps. Those who don’t attend can still find social support in local meetups and other study groups, but Bootcamps certainly come with their community-building methods. Bootcamps may offer career services as part of their package, and will overall provide a comprehensive and practical machine learning experience that is immediately applicable in the workplace. 

Best Ways to Learn Machine Learning Without Difficulty

There are several ways to learn machine learning without too much difficulty. One effective approach is to outline your current skill level, your availability, your financial capacity, and your preferred learning style. If any of these things are a struggle, they will impact your ability to focus on your education, which will make learning more difficult. Once you’re fully aware of your own boundaries or constraints, you can pick courses that are truly appropriate for you. Familiarizing yourself with the basics before fully committing to a course can help ease the transition into more structured learning, but knowing where you’re at and where you’re going is vital to making sure your resources are a fit for you.

Another effective approach is live training, which offers numerous advantages that can smooth out the learning process. Live online training is a great way to gain diverse perspectives on modern machine-learning techniques and provides an interactive environment where students can ask questions and get immediate feedback in response. This real-time interaction can significantly shorten the amount of time it takes to pick up core skills, and correct behaviors. Difficult concepts become manageable. Access to experienced trainers and industry professionals is invaluable in shortening the time it takes to learn. 

It will take time to become comfortable in machine learning. Live instruction can give you the experience in hands-on and real-world projects needed for you to make that jump from a newer learner, to an experienced professional. In addition, the structured environment and access to knowledgeable industry professionals can significantly help your retention of difficult concepts. Though live training is more expensive and time-consuming, the benefits are noticeable and often outweigh any drawbacks, making it the ideal way to master machine learning.