6 Methods You Can Use To Learn More About Machine Learning

As a relatively new technology, machine learning may not be something you're familiar with. If you want or need to become more familiar, there are many resources available to assist you. Here are six methods you can use to learn more about machine learning.

1. Start With Concepts And Prerequisites

The first thing you should do when you begin learning about machine learning is to study the concepts associated with machine learning and the prerequisites you need to begin working with these tools. Make sure you understand concepts such as targets, models, predictive analytics vs machine learning, reinforcement learning and supervised and unsupervised learning. Think about what you need to know in order to work with or develop machine learning tools, including statistics, calculus, linear algebra and various programming languages, such as Python.

2. Practice Alone And In Groups

You can access many resources and study materials on your own, but it's also a good idea to look for ways you can practice in groups. Solo learning is a great way to solidify your grasp of concepts and prerequisites. Group activities such as study groups, classes and competitions allow you to think outside the box, develop your critical thinking skills and improve your recall and capabilities under pressure.

3. Study Common Models And Applications

Once you gather resources and learn about the prerequisites and concepts required to work with machine learning tools, you can get more granular in your studies. Start by studying common models for machine learning and the common applications of each model. Doing so will assist you in furthering your understanding of machine learning as it's applied in the real world. It can also help you figure out what it is you want to do with machine learning if you don't already know. Once you do that, you'll be able to delve into the specific resources and concepts you need to know for your work.

4. Enroll In Courses

One of the most traditional routes to learning about machine learning is to enroll in one or more courses. You can choose to enroll in university courses or programs, workshops, courses through your employer and online courses, among other options. Depending on the course you choose, you may be able to access one-on-one tutoring or group lessons. Your course should also provide you with various studying and practicing resources and opportunities. Courses are excellent options because they provide you with multiple and varied opportunities to learn about many aspects of machine learning, both theoretical and practical. You may choose from beginner-level courses, general courses and courses that pinpoint specific aspects of machine learning, among others.

5. Determine Your Knowledge Gaps

Even after you develop your base knowledge and get more granular, there are still likely to be gaps in your knowledge. You should periodically search out those gaps and work to fill them in with the necessary knowledge and practice. Develop lists of potential gaps and organize them according to your learning and application priorities. Then you can schedule the amount of time needed to learn what you need to know. As you gain more knowledge, you're likely to continue to find these knowledge gaps.

6. Enroll In Bootcamp Programs

Bootcamps tend to be shorter programs than courses, but incredibly immersive. You will receive large amounts of training, resources and opportunities to practice alone and in groups in a bootcamp. These programs offer the opportunity to gain and develop the practical skills necessary to work with machine learning tools and programs, so if you choose to enroll in one, you should try to make sure you already have the necessary knowledge of prerequisites and concepts used in machine learning.

There may be some learning strategies that work better for you than others. If you already know your learning style well, look for resources that fit your style. If not, then try a few different resources to figure out what works best for you.