How do Beginners Learn Machine Learning?

Machine learning refers to the use of algorithms that can turn data and other collected information into something learnable so that a machine can learn by itself without being explicitly programmed to. In the field of technology, machine learning is a highly valued skill and falls under the umbrella of artificial intelligence. As this field continues to evolve, machine learning is worth investing in for those who have a particular interest in technology. It is a relatively recent field, so finding out where to start can be daunting. It’s recommended that you go into it with a few key skills and basic concepts in your back pocket, including a programming language, a working knowledge of algorithms, and some familiarity with data sets. Knowing regular machine learning terminology can also be highly useful. Having experience in STEM-related courses can make learning easier, especially when it comes to math. Algebra, probability, and statistics are related closely to algorithms. If that sounds overwhelming, don’t worry. This article will walk you through the most important beginner skills needed to build a strong foundation in machine learning, and why they can be helpful in teaching you one of technology’s fastest growing skills.

Getting Started with Machine Learning

Once you are ready to take the jump into machine learning, you’ll want to make sure you have the right tools. Machine learning requires certain hardware. Having access to a decent laptop or desktop will be vital to working without issue. Ensure that your computer has sufficient RAM (recommended 8GB to 16GB) and a modern processor. For those who intend to go into the intermediate and advanced levels of machine learning, a GPU can significantly speed up model training. A NVIDIA GPU can perform complex computations quickly on large amounts of data, making it an ideal addition to any projects that require it. Cloud-based services like Google Colab offer free computing resources including GPUs and TPUs. Securing these items before you begin your work can make your learning much more efficient.

Machine learning also regularly uses programming, so having access to libraries and frameworks for popular programming languages will make the process easier. Python is a great place to start. Common libraries include NumPy, Matplotlib, and Scikit-learn. There are also specific libraries that can help with managing machine learning algorithms. For those who intend to work in a collaborative setting, having access to Slack, Discord, or other chat-based platforms can help with asking questions and finding quick solutions to any problems that arise. Google Drive and Dropbox are perfect for sharing data sets and project files, so creating a free account early on in the process is recommended. 

Additionally, having a basic understanding of linear algebra, calculus, and statistics can provide a strong foundation for grasping machine learning concepts early on. Different machine learning courses will require different prerequisites for joining, so it is important to double-check which information you might need to comfortably manage the workload. Most requirements can be found on the course description page, and are important to look over. Some courses will require that you know Python beforehand, some will require math skills, and others will merely want you to have a general understanding of machine learning principles.

The First Steps When Learning Machine Learning

As a beginner, you’ll learn several skills that are essential for building a solid foundation in machine learning. With these, you can progress to intermediate and advanced levels later on down the line without issue. One of the first skills you’ll learn is how to be a programmer. Programming is essential to machine learning because it allows you to automate repetitive tasks and implement machine learning algorithms. Python and R are widely used in the field of artificial intelligence because of their simplicity and the large amount of resources available to them. Other languages that might be useful include Java, JavaScript, and C++. These programming languages aren’t only used in machine learning. They are also used in a variety of technology-related fields. While you’ll learn how to tailor your programming skills to machine learning, they are transferable to other industries as well, making them a valuable addition to any professional portfolio.

Early on in your machine learning career, you’ll learn more about data processing and machine learning algorithms. If you are already familiar with statistics and probability, then working with data won’t be too difficult. Understanding how to summarize and parse through data is vital to comparing different algorithms and making choices and adjustments as needed. Data processing techniques such as data cleaning and normalization are great ways to make sure that your data is relevant and high quality. In addition to this, grasping other important algorithm-related concepts like linear regression, decision trees, and k-nearest neighbors will help you to solve machine-learning problems. From there, you can build models that can do a variety of things, like make predictions or find important patterns in the data. Once you have tackled these basic skills, they can be built on and used in higher-level machine-learning courses.

Free Machine Learning Tools for Beginners

Several free machine-learning tools are well-suited for beginners. Sci-Kit Learn is a great starting place from a programming perspective. It is a free and open-source Python library for machine learning. It offers data analysis and data mining tools, built on NumPy, SciPy, and Matplotlib, which are commercially usable. Documentation and tutorials are available on the website, making use of the software easy. Google also offers a program called TensorFlow, which is an open-source library specifically geared towards machine learning. With the help of this program, students can create machine learning models capable of running in every environment. TensorFlow lite is a good alternative for those using mobile machine learning models, and there are guides and tutorials available on the website for those who are new to the program and want a closer look at its functionality.

Google Colab is a free cloud service that helps AI developers write and execute Python code. Through this service, users will have access to free computing resources like GPUs and TPUs, and its services require no setup and no subscription fee. This platform is particularly popular with machine learning researchers and practitioners, as it can be used in real-time collaboration on projects, and is a great place to store and access project-related data. 

Microsoft Azure has a free tier for their Machine Learning Studio. This tool is great for constructing and using machine-learning workflows, and also for building and testing predictive data analytics. This studio integrates with both Python and R, which is convenient for those who are using the more common programming languages for their machine-learning projects. With this software, anyone can build machine-learning models with solid and powerful AI infrastructure. These, alongside other tools like IBM’s free tier of their Watson Studio, and RapidMiner, can help with a variety of machine learning processes, from data processing to building full and functional machine learning models.

Live Machine Learning Training for Beginners

There are several distinct benefits to taking a live machine learning class. The first is the real-time interaction and feedback you get from having an instructor in class with you. Questions can be answered immediately, and students can receive clarification in a shorter time frame, making it easier to understand concepts and pick up material. There is also often a higher level of engagement. Instructors have a chance to review their student’s confidence in the material and revisit items that might be causing trouble. An in-class or live learning environment is a structured learning environment that is great for supplying a schedule, and a list of milestones or goals for students to work towards. There are systems of accountability that are more easily enacted when the students, peers, and instructors are in the same place together.

There are also plenty of hands-on activities in live classes that can benefit learners who enjoy tangible projects. With the help of guided exercises and demonstrations, students can apply concepts that they’ve learned in their lessons directly to their work. Projects are assigned and the instructor can help with problem-solving in real time, correcting or troubleshooting any issues. This networking can be advantageous for those in live classes. Not only is meeting your peers and developing your network useful for your overall professional prospects, but getting to shake hands with industry professionals, and gain an understanding of machine learning right from the source, is worthwhile.

It can’t be understated how important support and mentorship are to learning a complex and technical topic like machine learning. Being an active part of a learning community is vital to growing collaborative skills that will become important in your career. Getting regular encouragement from peers who are on a similar track can help keep you motivated and on-track. With the help of comprehensive learning tools that a live course can bring, like facilities, computer labs, programs, or software, they’re a great way to access supplemental materials on an as-needed basis. If you’re looking for an interactive environment that can enhance learning for beginners, a live course is highly recommended, and a great place to start.

The Next Step

Once you’ve become comfortable with the basics of machine learning, you’ll be able to move on to intermediate and advanced concepts. Those include deep learning, or the use of neural networks and transfer learning. There are also the concepts of reinforcement learning and unsupervised learning that are much more difficult for those without a solid basis in machine learning to understand. Finishing your beginner courses will provide you with opportunities to work with algorithms, but taking advanced courses will help build your confidence to use them in unique and personal settings. Similarly, at the higher levels of learning, you’ll be working closely with sophisticated models and frameworks that can help with things like predictive analytics, or other parts of your machine-learning projects.

There are advanced courses of all types, including live in-person and online options, as well as other formats like bootcamps, one-day courses, workshops, seminars, or private tutoring opportunities. As the information you learn becomes more complex, it is recommended that you take learning opportunities that come with a community component, as this can help cover the highly technical portions of machine learning, and make them easier to understand. It also helps with asking questions and getting quick answers.

The next step also includes advanced machine learning training, which can both widen your skill set and boost your career opportunities. Advanced courses come with certificates and certifications that can expand your professional profile and enhance your resume. With the correct training, you may qualify for more advanced roles in the field which means higher pay and access to cutting-edge technology. Networking becomes a crucial part of machine learning as you transition into the workforce. Collaboration with industry professionals and researchers can also help you situate yourself in the field. The next steps after you pass your beginner level are not the same for everyone, but there are many options. Building a strong foundation is critical in picking the best next step for you.