What is the Best Course to Learn Machine Learning?

Machine learning is a highly technical skill in a rapidly growing field. There are many courses available, and that can make it difficult to choose the best one. Choosing the best course is a highly subjective and personal decision, and certain factors can make that decision easier. Things like course cost, format, and duration can give you an overview of how closely a program aligns with your overall educational goals. This can help you make a more informed decision so that whichever course you take is best for you.

Course Cost

The first consideration that many people make when choosing a course is how much it will cost. Because machine learning classes can get quite pricey, knowing how much you’ll need to spend can be useful. While factors like the course format, amenities, resources, and hosting institution can impact how much you have to pay, a general in-depth course will cost anywhere from $1,800 to $5,000, with some programs being even more than that. When you add this to necessary subscriptions, software, and technical requirements, managing the financial side of taking a class is necessary for many people. There are cheap or no-cost solutions, as certain platforms like YouTube or OpenML will have resources and communities that can assist you in fundamental machine learning and artificial intelligence skills. However, as you move into more advanced and professional content, a paid course is highly recommended, as the content is vetted and approved by recognized institutions. When considering the course cost, it is best to first find a list of classes in your financial range. From there, you can take your other learning preferences, like the format and duration, and find a class that aligns with those without breaking the bank.

Course Format

Machine learning courses come in several formats. The one that’s best for you will be highly dependent on your educational needs. These classes are offered both in-person and online, with the in-person classes being ideal for those learners who enjoy the face-to-face component of classwork. Despite there being many social benefits to in-person learning, live online learning is the virtual equivalent, with instructors and classmates being online at the same time. This can provide the ultimate comfort in terms of location, while also filling the social component. For those who need ultimate flexibility, online courses are offered asynchronously, which means that they are self-paced and taken at the student’s discretion. Machine learning is heavily tech-dependent, which can make online learning particularly advantageous, but it’s still important to consider the way you learn best. When visiting an institution for an in-person course, double-checking the facilities can help you better understand the amenities that come with your course. This can include access to machinery and technology, books, and other resources exclusive to on-site opportunities. Similarly, some online classes have exclusive content that can only be accessed through that program. Choosing between in-person and online is difficult, but machine learning can be done well with both, so reviewing the personal pros and cons of each format will be important to choosing the best course.

Course Duration

Similar to the financial aspect, taking a course is much more difficult if you do not have the required time available. Understanding the schedule is vital to choosing a course that’s best for you. The time commitment for a machine learning course is dependent on the course type. University courses and bootcamps will take anywhere from 8-12 weeks to complete unless they are one-time courses, workshops, or seminars. There are shorter courses available. Some can go from 1-2 weeks, while others are only a day long. The course duration is aligned with not only your proficiency but also your overall educational goals. For those getting a degree in computer science, data science, or software engineering, that can be a years-long pursuit. When choosing which machine learning course is best for you, taking into account the commitment to the program is important. For those who have more open availability, longer courses are a quicker way to learn the content. For those with more obligations, though part-time classes may take longer in terms of learning, they can accommodate a more unique schedule, allowing you to pick up vital skills without compromising your personal or professional obligations.

Educational Goals

Educational goals are arguably one of the most important parts of choosing the best machine learning course. Each program you consider will have its curriculum, and it’s important to double-check what is being taught against what you want to learn. Proficiency level can be a good starting point. Introductory machine learning topics will include machine learning concepts and terminology, basic programming skills, and algorithms. The intermediate and advanced levels will teach machine learning libraries, data cleaning, and model evaluation, as well as deep learning and neural networks. Each institution approaches machine learning differently, so it’s important to double-check what an institution offers in a class, and how that can help you reach your personal and professional goals. Many times, a detailed curriculum can be found on a course’s webpage. The curriculum may be impacted by the course type, so one-day classes will have a much shorter list of subjects than a bootcamp or a university course. If you’re looking specifically for professional growth, a certification program or a professional training center will offer classes that are more closely aligned with what you’re looking for, which would make them a better fit for you overall.

Learn Machine Learning with Noble Desktop

Noble Desktop offers a Classes Near Me tool that can help you find machine learning courses in your area. This tool is useful in helping you compare your options. The results of your search will be both inside and outside of the Noble Desktop network, but if you’re looking specifically for those courses offered by Noble Desktop, there is a Python Data Science & Machine Learning Bootcamp. Python is a skill needed in many industries and fields, including computer science, artificial intelligence, and other data-based careers. This bootcamp will cover algorithms and in relation to data science and machine learning. You will learn how to handle common machine-learning problems and gain hands-on experience with problem-solving. This course also covers statistical concepts like bias, variance, and overfitting. 

Python for Machine Learning is one of the two classes offered as part of the Python Data Science & Machine Learning Bootcamp. This standalone course is perfect for learning more about the role of programming languages in machine learning. The course starts with fundamental concepts like regression analysis, classification, and decision trees, before moving on to using the Pandas library to clean and balance data and apply machine learning algorithms. You can pick up other important concepts like overfitting, variance, and bias, and at the end of the course you will have a final portfolio that can help with your transition into the professional workforce. It is recommended that students of these bootcamps have familiarity with Python and its data science libraries, NumPy and Pandas. If you need to brush up on those skills, consider joining Noble Desktop’s Python Programming Bootcamp. It will provide a thorough overview and help you build confidence in your programming skills.