Technology transforms the world in so many ways. Ford’s introduction of the assembly line was essential to the vehicle manufacturing process. The introduction of the internet changed how we communicate, do business, and interact with the world. And in machine learning, we have an emerging technology that transforms how we use computers to complete complex tasks.
Think of machine learning models as “brains” that machines use to actively learn. No longer constrained by rules laid out in their programming, machines have the ability to develop an understanding of new concepts and deliver analysis in ways they never could before. And as a prospective machine learning student, you can become the person who creates the “brains” that modern machines use now and in the future.
But you need a good starting point before you can do any of that. This article covers three of the best machine learning tutorials for beginners who want to get their feet wet while building foundational knowledge that serves them in more specialized courses.
Factors to Consider When Choosing a Machine Learning Tutorial
A machine learning beginner can’t expect to jump straight into a course that delves into neural networking and deep learning and have any idea what they’re doing. They need to learn to crawl before they can walk, making the following factors crucial to consider when choosing a machine learning tutorial for beginners.
- Content quality. You wouldn’t use cheap plastic parts to build an airplane, just like you can’t rely on poor-quality course content to get you started with machine learning. Always look for reviews of a tutorial before engaging, in addition to checking the credentials of the provider to ensure they deliver relevant content that aligns with your career goals.
- Instructor expertise. Sticking with our airplane analogy, imagine being taught how to pilot a plane by somebody who’s never actually flown. It simply wouldn’t work. The same goes for a machine learning tutorial, as you need to see evidence that your instructor does more than parrot information that you can find elsewhere. Look for real-world experience and accreditation from recognized authorities.
- Course structure and pacing. As nice as it would be to have an infinite amount of free time to dedicate to learning, that isn’t a reality for anybody. You have work, life, family, and possibly other study commitments to keep on top of, and your machine learning tutorial has to fit around all of it.
- Practical and real-world examples. Theoretical knowledge can only take you so far. You need to know how to apply what you’ve learned, which is why a good tutorial should have practical elements that test your knowledge. Think of it like driving a car. You can read pages upon pages of material on how to drive properly but you won’t be able to get on the road until you’ve spent time learning behind the wheel.
- Community support. Machine learning is a complex subject and it’s natural to feel a little lost with the materials in many tutorials. A strong community gives you a resource base to lean into, in addition to exposing you to peers (and experienced tech-heads) who can help you along or point you in the right career direction.
Top Three Machine Learning Tutorials for Beginners
Now you know what to look for in a machine learning tutorial for beginners, you’re ready to start searching for a course. But if you want to take a shortcut and jump straight into learning, these three courses are superb starting points.
Tutorial 1 – Intro to Machine Learning (Kaggle)
Offered at no cost, Intro to Machine Learning is a three-hour self-paced course that allows you to learn as and when you feel like learning. All of which is helped by Kaggle’s clever save system. You can use it to save your progress and jump back into your learning whenever you’re ready. The course has seven lessons, the first of which offers an introduction to machine learning as a concept. Whereas the other six dig into more complex topics and come with an exercise for you to complete.
Those little exercises are the tutorial’s biggest plus point. They force you to apply what you’ve learned before you can move on to the next lesson. The course also has a dedicated community (led by tutorial creator Dan Becker) that can help you if you get stuck. You even get a certificate for completing the tutorial, though this certificate isn’t as prestigious as one that comes from an organization like Google or IBM.
On the downside, the course isn’t a complete beginner’s course. You’ll need a solid understanding of Python before you get started. Those new to coding should look for Python courses first or they’ll feel lost when the tutorial starts throwing out terminology and programming libraries that they need to use.
Ideal for students with experience in Python who want to apply the programming language to machine learning models.
Tutorial 2 – What Is Machine Learning? (Udemy)
You can’t build a house without any bricks and you can’t build a machine learning model before you understand the different types of learning that underpin that model. Those different types of learning are what the What is Machine Learning tutorial covers. You’ll get to grips with supervised, unsupervised, and reinforcement learning, which are the three core learning types a machine can use to feed its “brain.”
The course introduces you to real-world problems and helps you to see which type of machine learning is best suited to solving those problems. It’s delivered via online videos, totaling just under two hours of teaching, and includes demonstrations in Python to show you how each type of learning is applied to real-world models. All the resources used for the tutorial are available on a GitHub page (which also gives you access to a strong online community) and the tutorial is delivered by an instructor with over 27 years of experience in the field.
It’s not the perfect course, by any means, as it focuses primarily on learning types without digging much deeper. Those looking for a more in-depth understanding of the algorithms used in machine learning won’t find it here, though they will build foundational knowledge that helps them to better understand those algorithms once they encounter them. As an Udemy course, it’s free to take but requires a subscription to the service if you want a certificate and the ability to communicate directly with the course provider.
Ideal for students who want to learn about the different types of machine learning and how to use Python to apply them.
Tutorial 3 – Machine Learning Tutorial (Geeksforgeeks)
As the most in-depth machine learning tutorial for beginners, the Geeksforgeeks offering covers almost all of the theory you could ever hope to learn. It runs the gamut from a basic introduction to machine learning through to advanced concepts, such as natural language processing and neural networks. And it’s all presented via a single web page that acts like a hub that links you to many other pages, allowing you to tailor your learning experience based on what aligns best with your goals.
The sheer volume of content on offer is the tutorial’s biggest advantage, with dedicated learners able to take themselves from complete machine learning newbies to accomplished experts if they complete everything. There’s also a handy discussion board that puts you in touch with others taking the course. Plus, the “Practice” section of the tutorial includes real-world problems, including a “Problem of the Day” that you can use to test different skills.
However, some students may find the way the material is presented to be a little disorganized and it’s easy to lose track of where you are among the sea of materials. The lack of testing (barring the two or three projects in the “Practice” section) may also rankle with those who want to be able to track their progress easily.
Ideal for self-paced learners who want to be able to pick and choose what they learn and when they learn it.
Additional Resources for Learning Machine Learning
Beyond tutorials, there are tons of additional resources you can use to supplement your learning. These resources are essential for continuing your education because machine learning is an evolving concept that changes constantly.
- Books. Machine learning books are great for digging deeper into the theory you learn via a tutorial, though they come with the downside of offering no practical examples or ways to interact with authors.
- YouTube channels. YouTube videos are ideal for visual learners and they tend to offer a free way to build on what you learn in a tutorial. Examples of great channels to check out include Sentdex and DeepLearningAI, with both channels covering emerging trends in the field alongside lectures and tutorials.
- Blogs and websites. Blogs come with the advantage of the communities that sprout up around them, which you can rely on to build connections and further your knowledge. Of course, there’s the information shared in the blogs, too, though you must check the writer’s credentials before digging too deep into their content.
Master a Machine Learning Tutorial for Beginners Before Moving On
A machine learning tutorial for beginners can give you a solid base in the fundamentals of an extremely complex subject. With that base established, you can build up by taking other courses and tutorials that focus on more specialized aspects of machine learning. Without the base, you’ll find the learning experience much harder. Think of it like building a house – you can’t lay any bricks until you have a foundation in place.
The three tutorials highlighted here give you the base you need (and more besides), but it’s continued study that’s the key to success for machine learning students. Once you’ve completed a tutorial, look for books, blogs, YouTube channels, and other courses that help you keep your knowledge up-to-date and relevant in an ever-evolving subject.
Related posts
Life is unpredictable. While many of us have specific hopes and expectations of how our futures will turn out, things don’t always go as expected. There are many variables and unexpected incidents that can interfere and force you to alter your plans, and this is particularly true when it comes to education.
For instance, you might have had plans to study a specific subject, but had to deviate from those plans due to unforeseen circumstances. Or you may have had to enter the workforce in an occupation different from the one you sought in an effort to earn an income, a move that may not provide you with the time or opportunity to achieve your desired educational aims.
In short, every individual’s career pathway is different, and very few go exactly as we expect from the outset. Fortunately, even if you experience a few false starts or sudden twists in your pathway, there are always options available to help you get back on track.
The Unpredictable Nature of Education
In theory, the educational process seems simple. You select a course of study that interests you from the vast array of subjects and prospective professions, select the appropriate classes, acquire the knowledge and skills you need to succeed, and then embark on your profession.
In reality, however, as many people know from firsthand experience, the road to education is often far from straightforward. Here are just some of the many challenges that can take your educational path in a completely different direction:
- Life’s Curveballs: As touched on in the introduction, life is impossible to predict. Financial hardships, health issues, and family emergencies are just some of the unfortunate occurrences that might derail even the most perfectly planned educational regime.
- Changing Interests: People’s desires and preferences don’t necessarily stay the same throughout their entire lives. As you grow, learn, and have new experiences, your interests may change, and so, too, may your educational objectives.
- Pressure and Burnout: Some academic paths are particularly challenging, demanding intense levels of study and hard work. This can sometimes prove too much to bear, even for the most resilient students.
- Failures and Setbacks: Conventional education largely builds around tests and examinations, requiring students to demonstrate their competencies repeatedly. It’s a system that doesn’t suit everyone, and test failures can lead to setbacks and delays.
- Inequality: People can be born with very different privileges and levels of access to education. Those in certain parts of the world may find it much more challenging to complete their education path for financial, cultural, or even political reasons.
- Late Bloomers: People develop at different paces. Some may struggle educationally early on in their lives, forcing them to make certain concessions or sacrifices related to their studies, only to find their feet later in life once they’ve entered the world of work.
Whether you’re a late bloomer, have had your educational aspirations delayed by personal problems, desire to learn new skills and try something different, or want to begin a fresh chapter in your professional life, the Open Institute of Technology (OPIT) may be able to help.
Introducing OPIT
OPIT is an online teaching platform, making high-level technological educational programs accessible to all, no matter their age or background. Offering education in fields like computer science, artificial intelligence, and digital business, OPIT provides a curated collection of degrees. In addition, they offer classes taught by world-leading tutors imparting the wisdom and skills students need to achieve their goals and become the tech leaders of tomorrow.
Meanwhile, for those who have had somewhat tumultuous or unpredictable educational paths, OPIT offers the perfect course corrector: the OPIT Foundation Year.
The OPIT Foundation Year
OPIT’s Foundation Year is a Pre-Tertiary Certificate in Information Technology, fully aligned with MQF/EQF Level 4 standards and valued at 60 ECTS credits. Lasting just one year, this program essentially serves as a comprehensive yet accessible springboard towards higher-level education, creating a path towards degrees and careers in dynamic, flexible fields, like computer science and digital business.
Like other OPIT programs, the Foundation Year is delivered entirely online via the OPIT Virtual Learning Environment. Combining live lectures, asynchronous content, and interactive assessments, students enjoy diverse and dynamic study experiences, acquiring core skills like academic writing, mathematics, and computer literacy, and building a bedrock of knowledge and confidence before taking their next steps.
Who Is the Foundation Program For?
The Foundation Program is designed to provide a solid base upon which to build the technological education many students need. It’s the perfect choice for those who are eager and ambitious to enter professions in AI, data science, and computing, but don’t feel that they have the necessary core skills and knowledge needed to dive straight into a degree.
Entry requirements are relatively relaxed in order to allow as many students as possible to enjoy the benefits of this program. With that said, applicants should ideally hold an MQF/EQF Level 3 or equivalent qualification, with the intention of pursuing a bachelor’s degree. A minimum of B2 level of English proficiency is also required, as this is the working and studying language of the institution.
What the Foundation Year Provides
Perhaps you’ve recently graduated, are considering a career change, or finally have the opportunity to return to education after initial delays or unexpected disruptions to your original plans. Either way, the Foundation Year can help you enjoy:
- Greater Self-Confidence: Foundation Year graduates gain the fundamental skills they need to enter degree programs with much more self-belief and assuredness.
- Superior Tech Knowledge: Lasting two terms, this course explores mathematics, academic reading and writing, and provides an introduction to computer hardware and software.
- Foundational Mathematics: Mathematics literacy forms a large part of the study focus for the Foundation Year, helping students feel more comfortable with numbers and formulas.
- Flexible Learning: Unlike more rigid, conventional education environments, OPIT gives you the freedom and flexibility to study at a pace that suits you best, all from the comfort of home.
- Global Community: OPIT is an international institution, with staff and students from all around the world eager to share knowledge, exchange ideas, and help one another.
Take Your Next Steps to Success With the OPIT Foundation Year
If you’re curious about a career in technology or have always wanted to work with AI, data, and computers, but struggled to find the time and opportunities you need to acquire relevant skills and knowledge, the Foundation Program was made for people like you.
It’s the ideal entry point into the exciting world of online education, and the perfect first step towards a prestigious degree from an innovative and increasingly successful institution. Download the brochure to learn more about it, or start your online application, today.
Students today have a broader range of fields of study to choose from than ever before, but with the world becoming increasingly technological and computers increasing in influence and importance, pursuing a career in computer science often proves a smart, strategic choice.
There are numerous benefits and career paths associated with studying and working in computer science, and we’ll be listing just a few of them in this guide.
High Average Salaries
With the rising cost of living in many parts of the world, it’s unsurprising that many students are thinking several decades ahead to determine what level of starting salaries they could obtain in different career fields.
Many are also seeking professions that offer the opportunity for growth and the ability to advance up the ranks over time, thus increasing their salary and their quality of life in the process.
If a strong, stable salary with the opportunity for improved income is one of your top career priorities, computer science should be at or near the top of your list of prospective careers.
According to recent data, computer scientists earn an average of over €65,000 per year, with certain jobs, like IT project leader and data scientist, paying ever higher. Starting salaries are strong, too, with graduates earning anywhere from €46,000 to €60,000, depending on their chosen profession and level of qualifications.
There are similarly high average salaries reported around the world in computer science and related fields such as data science and AI/ML engineering. These numbers are projected to increase in the years to come, pointing to computer science as a way for graduates to get off to the best financial start of any career.
Unrivaled Flexibility
A common problem with some subjects and courses is that they only provide graduates with a narrow set of skills and a similarly narrow range of potential professions to which they can apply those skills.
That’s not the case with computer science. Graduates in this field can enjoy instant access to a remarkably diverse array of career opportunities, with even newer opportunities being created all the time as technology evolves and innovations emerge.
A few of the many industries and roles you might choose to enter in the field of computer science include:
- Healthcare: As a software developer, data analyst, or cybersecurity expert
- Finance: As a fintech engineer, blockchain developer, or security analyst
- Media: As a graphics programmer, AI developer, or game developer
- Education: As an analyst, software developer, or machine learning engineer
Guaranteed Opportunities
Some career paths are more limited than others, with relatively low numbers of opportunities, recurring risks of job loss, or difficulty obtaining employment in the first place.
Again, with computer science, this simply isn’t the case. At a time when 75% of companies plan to embrace AI and other technologies by 2027, and businesses of all sizes and industries are now relying on computers more than ever before, computer science graduates can enjoy almost unbeatable job security.
Opportunities will continue to grow for people with good computing knowledge, whether that be in the obvious fields like software and web development, engineering, and AI development, or more niche sectors.
With so many options, you’re effectively guaranteed a long, rewarding career if you put in the necessary time and effort needed to establish a strong foundation of computing skills.
Rapidly Expanding and Evolving Industries
It’s no secret that the world of technology is a dynamic and fast-moving one. In the past 20 years alone, we’ve seen the proliferation of the internet, the rapid advancement of smartphones and wearable devices, the emergence of AI, and so much more.
In the years ahead, engineers and developers will continue to explore the boundaries of modern technology’s powers and potential, driving new innovations and improvements and opening more exciting job opportunities for those already established and experienced in this field.
Getting into computer science now could therefore provide a solid foundation for a career filled with excitement as you uncover and explore new ways of working with computers in fields as diverse as healthcare, finance, education, entertainment, manufacturing, logistics, and beyond.
Start Your Computer Science Career at OPIT
We’ve discussed some of the many benefits associated with careers in computer science, from the high starting salaries to the rapidly growing array of job options available to graduates. By now, you may be eager to follow this particular career path and take advantage of the wealth of opportunities.
If you’re wondering how to start, the Open Institute of Technology (OPIT) may hold the answers you need. As an exclusively online learning platform, specializing in computer science and digital business, and staffed by some of the world’s leading tech experts, OPIT is producing the tech leaders of tomorrow.
Some of the many advantages of learning with OPIT include:
- Accessible and flexible online education that matches your learning style and schedule
- Heavy focus on real-world applications of the skills you acquire
- An international community of like-minded students from around the globe
- A top team of tutors and lecturers from varying fields and industries
- Progressive assessment of skills and understanding – not constant exams
OPIT offers a small, curated selection of courses for those seeking to gain the technological skills and knowledge to succeed in their chosen areas of expertise. That includes a BSc (Hons) in Computer Science – a six-term program, worth 180 ECTS credits.
Numerous IT industry leaders and experts helped develop this fully accredited undergraduate degree, which is online and accessible to all. Like other OPIT courses, it offers a flexible learning program, with progressive assessments, fast-track options, and the opportunity to be part of a growing community of learners and tutors.
If the benefits of doing a career in computer science interest you, download the OPIT BSc in Computer Science brochure or fill out an online application today and take your first step toward a rewarding and fulfilling profession.
Have questions?
Visit our FAQ page or get in touch with us!
Write us at +39 335 576 0263
Get in touch at hello@opit.com
Talk to one of our Study Advisors
We are international
We can speak in: