With immense pride and anticipation, we announce the inaugural event for the OPIT – Open Institute of Technology academic year. As pioneers in the new era of Higher Education, this event encapsulates the very ethos of what OPIT represents. Not just an event, but the commencement of a journey to pave the way for the next generation of leaders in the field of IT.

Event Details

• Date: September 12th, 2023
• Time: 5.00-6.00 PM CEST
• Platform: Online
• Registration: Link

Event Schedule

1. Official Introduction: Mr. Riccardo Ocleppo, the founder of OPIT, paints a picture of the Institution’s foundational pillars and what prospective students can expect from their academic journey.
2. Learning Model Presentation: Prof. Francesco Profumo, our esteemed Rector, delves deep into the heart of OPIT’s avant-garde learning experience, shedding light on its core tenets and alignment with the demands of the contemporary job market.
3. Accreditation and Quality Assurance: The Malta Minister of Education, Dr. Clifton Grima, offers insights into the robust educational framework of Malta and the stringent quality assurance measures in place.
4. The Future of Jobs in the Era of AI: Prof. Alexiei Dingli navigates the evolving terrains of the job market under the shadow of AI’s relentless march, emphasizing the pivotal role of institutions like OPIT.
5. The Impact of Digitalization on a Global Scale: Dr. Bernardo Calzadilla Sarmiento, former Managing Director of UNIDO (United Nations Industrial Development Organization) offers a panoramic view of the digital revolution sweeping across the globe and its profound implications on industry, economy, and education.
6. Q&A Session: Led by Greta Maiocchi, the Head of Admissions at OPIT, this segment is dedicated to addressing queries, clearing doubts, and facilitating an open dialogue.

In a world where AI and digital innovation are reshaping boundaries, institutions like OPIT emerge as guiding lights. Join us at this pivotal juncture as we navigate the AI-driven future, fortified by our dedication to education, foresight, and ambition.

Join us in marking the beginning of an era. Let’s shape the future, together.

Register here for the event.

For 68% of Italian students, the perfect training opens up the world of work and connects them to companies. And 72% of students prefer the hybrid educational model.

The data comes from a survey of 1,600 members of the Docsity community by OPIT – The Open Institute of Technology.

OPIT founder Riccardo Ocleppo states: “Students need more practical learning and skills that allow for a faster and more profitable entry into a company.”

Milan, 19 June 2023 – Italian students aged between 18 and 26 prefer educational and training offerings based on the hybrid models and a focus on up-to-date training provided by quality teaching staff. They’re also less likely to believe that the name of a university is enough to guarantee job opportunities upon graduating. These are some of the chief findings to emerge from an OPIT survey of 1,600 students (secondary level and university) who are part of the Docsity community – a platform for sharing documents and interesting content – just a few days before the beginning of final exams.

The results show that students consider job opportunities and connections with companies as the main factors when evaluating study opportunities (68%). Cost is also an important criterion (39.6%), as is the updating of teaching methods and practical aspects of the course to ensure they’re aligned with today’s work environment (33.1%). Furthermore, 21.7% of those surveyed note the quality of the teaching staff as being crucial to helping them absorb the skills they need to succeed as workers in the future. The “name” and reputation of a university of training provider only matters to 13% of those surveyed.

“The data confirms what we had foreseen when we decided to enter the education market,” says OPIT’s founder and director Riccardo Ocleppo. “Involving companies in our programs was a top priority, and their insights were instrumental in designing the modules we created, including what technologies to rely on and the programming languages we work with, for example.”

“By working with companies to design our programs, we’ve found that students both require and prefer a much more hands-on learning experience. This ensures they’re up to date on current technologies, processes, and ways of working when they join a company. So, our goal for our students is that they leave OPIT feeling much more knowledgeable about what employers really need from them.”

As far as learning methods are concerned, students prefer the hybrid model – having the opportunity to participate in face-to-face lessons while retaining the flexibility to access course content online or even via a fully remote model based on their needs.  Amongst university students, 72.6% say they prefer the hybrid model, unlike secondary students, who retain a preference for my “physical” styles of teaching.

When secondary students were asked about their choice of university, 46% of boys and girls indicated engineering, computer science, and STEM as their preferred fields. Humanities and communication followed (20.6%), with economics taking the third spot (17.9%).

“Rapid developments in technology and artificial intelligence,” continues Ocleppo, “are creating new job opportunities for STEM graduates, which current students clearly understand. Specific skills are becoming increasingly important as enterprises move more and more to make the most out of the changes brought by AI. Yet, the shortage of tech workers is expected to grow even faster in the coming years. Despite the concern that the wave of AI-inspired technologies is creating, there is no doubt there will be demand for certain types of professionals with specific technical skills.”

OPIT’s data also indicates a widespread trend toward the continuation of studies beyond initial certification, belying the more pessimistic readings on the growth of the NEET (Not in Education, Employment, or Training) phenomenon. Enrolling in a degree course remains both the safest and preferred choice for the majority of secondary school students – 82% confirmed their intention to continue their studies at the university level. A further 8.3% are undecided about university, while 5% will choose short training courses, with only 2.5% of students surveyed saying they’ll stop education after their fifth-grade exams. Accredited training (university, business school, or some other form of higher education) remains the preferred choice of almost all students (94.6%).

Delving deeper into a behavioral analysis of university students, an interesting preference for further continuation of studies emerges. Over two-thirds (68%) say they wish to continue, demonstrating that a Bachelor’s degree alone is not seen as the ideal pathway into the world of work. In fact, of those who declared a willingness to continue studying after submitting their Bachelor’s thesis, 90% said they want to enroll in a new long-term study program – either a second Bachelor’s degree or a Master’s degree. It’s also significant that more university students are undecided about continuing their educations (22%) than those who are convinced they’ll finish studying permanently upon completion of their degrees (10%).

Asked about what will be most important in a future where they will have to grapple with various AI-led transitions, over half of students (56%) believe it’s essential to understand artificial intelligence and its applications. This was followed by digital marketing (42%), with cybersecurity identified by one in three students (35%) as key due to the job opportunities in that field linked to the need to protect growing amounts of personal data. Fintech closed this ranking at 3%.

OPIT – Open Institute of Technology is an academic institution accredited at the European level that provides an exclusively online training offer focused on Computer Science and a teaching staff made up of professors of international standing. OPIT stands out in the panorama of university-level training for a didactic model shaped by the need for quality, flexibility, and connection with the business world of upcoming generations. OPIT’s degree programs are oriented towards the acquisition of modern and up-to-date skills in the crucial sector of computer science. Its degrees are accredited by the MFHEA and the EQF (European Qualification Framework), and professionally recognized by employers.

https://www.opit.com/ 

A Practical Guide to Thriving in Today’s Job Market Powered by AI and Computer Science

Click this link to read and download the e-book.

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.

If there’s an adjective that perfectly captures the world today, it’s data-driven. Without machine learning, we could never exploit the full potential of all this data that drives our personal and business decisions.

So, it’s no wonder many people are pursuing a career in machine learning.

To have a real shot at landing your dream job in this field, you must be certified as a data scientist or a machine learning engineer.

That’s where machine learning certification courses come into play.

These courses will help you acquire the necessary knowledge and skills to crush your certification exam and open up a world of possibilities for your future employment.

To help you find the best machine learning certification course, we’ll guide you through the proper selection process. We’ll throw in some tips on making the most out of the selected course for good measure.

If you don’t feel like researching, check out one of our top course picks and start your journey in the booming field of machine learning.

Factors to Consider When Choosing a Machine Learning Certification Course

Unlike machine learning algorithms, you might find it challenging to comb through all the data online and find the perfect machine learning certification course. But allow us to let you in on a little secret – once you know what you’re looking for, you’ll become as efficient as these algorithms.

Course Content and Curriculum

Looking past the title is essential when choosing the most suitable machine learning certification course. The course’s description includes all the good stuff. Here, you’ll find a laid-out curriculum listing all the course topics.

If you’re a beginner, seeing terms like “regression” and “clustering” probably won’t do much for your understanding of the course. But since you’re looking to get certified in the field, you may already have some experience. So, reviewing the course’s curriculum will help you determine whether it has what you need to pass your certification exam.

Course Duration and Flexibility

Online courses are all about flexibility. If you already have a job, you’re probably looking for something self-paced to fit your busy schedule. However, with scheduled courses, you can interact with the instructor directly. So, weigh all your options before making a final decision.

The course’s duration is also an essential factor. A machine learning certification course will likely last longer than a standard crash course, so make sure you can commit fully.

Instructor’s Expertise and Experience

Given the complexity of machine learning, an instructor’s expertise and experience are crucial for genuinely grasping this field’s ins and outs. In a machine learning certification course, these factors become arguably more important since your instructor will be something like a mentor to you during your education journey.

Course Fees and Additional Costs

The internet is a great place to find numerous incredible courses free of charge. If that’s what you’re looking for, you’ll be happy to know there’s no shortage of free machine learning courses. But the bad news is that these courses seldom come with a certificate, let alone a certification.

If you want to complete a machine learning certification course, be prepared to pay a relatively high fee. Think of these costs as an investment in your future.

Certification and Accreditation

Receiving a certificate of completion is relatively simple. You only need to go through all the lessons, turn in exercises, and complete a test or two. Certification, however, is on an entirely different level. A machine learning certification course aims to prepare you for passing a certification exam (which is notoriously hard to do), so choose only courses offered by certified individuals or accredited institutions.

Job Placement and Career Support

Sure, learning for the sake of learning is wonderful. Just think of all the personal growth and betterment it will bring you, and you’ll always want to foster a deep love for knowledge. But in a field as competitive and lucrative as machine learning, learning to enhance your career prospect is more than reasonable. So, before committing to a course, ensure it offers the practical skills and know-how you need to get a job shortly after.

Top Picks for Machine Learning Certification Courses

Check out our top three machine learning certification exams and the courses you must take to prepare for them.

AWS Machine Learning Learning Plan

Earning the AWS Certified Solutions Architect – Associate Certification can do wonders for your career in machine learning. With this certification, you gain valuable expertise in building, training, and deploying machine learning models on AWS (Amazon Web Services). But to pass this challenging certification exam, you’ll need a prep course.

Enter AWS Machine Learning Learning Plan.

This machine learning certification course was built by AWS experts to make you one as well. It’s beginner-friendly and consists of several short courses that eliminate the guesswork of exam prep.

You can take the course at your own pace. Also, you can skip some courses if you already have that area covered. The only downside is that the progress bar can change without your input as the company adds or removes training content, which can throw you off for a while.

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

The lengthy name of the course gives you all the basic information you need – you’re taking it to prepare for the Google Cloud Certification for a Professional Machine Learning Engineer title.

Since this certification is one of the hardest to obtain in the industry, this course, or a set of courses, will be a lifesaver. It starts slowly, with some cloud basics. Then, it gradually dives deeper, where more complex machine learning solutions await.

During the certification test, you’ll be asked to solve real-world problems using machine learning. But this course teaches you how to do just that. You’ll learn to create and deploy successful machine learning solutions for any challenge that lies ahead.

Some may view the length of this course as a downside. You’ll need around seven months to complete it (at a pace of five hours a week). However, the certification test is rather comprehensive, so the course has no other option than to follow suit.

Machine Learning Cornell Certificate Program

Unlike the options from Google and Amazon, this is an all-in-one course. In other words, the certification exam is a part of it. No machine learning experience is necessary to enroll in this course. Still, familiarity with some basic programming, math, and statistics concepts will do wonders for your progress.

This program aims to equip you with the practical skills to approach real-world problems, select the best machine learning solution, and implement it efficiently. You’ll practice with live data from the get-go, allowing you to get a feel for your future career immediately.

Although the lessons are self-paced, they must be completed in a pre-determined order. Learners with more experience might perceive this as a downside since they will be forced to go through even the familiar concepts again.

Essential Skills for Success in Machine Learning

Sure, a machine learning certification course is an excellent foundation for your career in machine learning. But you’ll need a robust skill set to thrive in this career.

• Programming languages. Machine learning is all about programming, so you won’t get far without knowing and improving programming languages like Python, R, C++, and JavaScript, to name a few.
• Mathematics and statistics. A solid background in mathematics (calculus, linear algebra, probability theory) and statistics (p-value, standard deviation, regression analysis, etc.) will make your job much easier.
• Data preprocessing and visualization. Machines don’t do all the work in machine learning, not even close. You’re the one that needs to preprocess data and ready it for analysis. The same goes for data visualization (using different libraries to spot and understand data patterns).
• Machine learning algorithms and models. As a data scientist, you’ll need to learn about numerous machine learning algorithms (like supervised and unsupervised learning) and models (like classification and regression).
• Model evaluation and optimization. Monitoring and assessing how well a machine learning model performs will be essential to your job. The same goes for optimizing those models that fall short.
• Deployment and maintenance of machine learning models. Knowing how to deploy models successfully and keep them accurate and effective are must-have skills in machine learning.

Tips for Maximizing the Benefits of a Machine Learning Certification Course

Your chosen course can give you all the necessary content to succeed. But only if you interact with it correctly. Here’s how to make the most out of a machine learning certification course:

• Set clear goals and expectations. Carefully consider which skills you can acquire within the course’s timeframe.
• Dedicate time for self-study and practice (ideally, daily).
• Work on real-world projects and build a portfolio. This is the fastest way to demonstrate your skills after completing the course.
• Engage in online forums and communities (within the course, on Reddit or Kaggle).
• Network with professionals in the field at conferences, workshops, and meet-ups.

Cracking the Code to Success

Whether going to tech giants and industry disruptors like Google and Amazon or accredited institutions like Cornell, a machine learning certification course is your one-way ticket to a successful career. After all, machine learning is one of today’s most in-demand fields.

Of course, this certification is only a beginning. What’s next? A fantastic journey of continuous learning, of course. This is the only way to remain in tune with this ever-evolving field.

Once a concept found exclusively in science fiction, machine learning has seen widespread use in the modern age. As soon as various industries grasped the potential of ML, this field of computer science turned into a staple of tech and other businesses.

Naturally, all this has led to an increased demand for machine learning experts. The job market abounds with offers for positions in the field, and the competition is fierce. In other words, you may find plenty of job openings for machine learning professionals, but you’ll need to fit the bill to actually land the position.

Fortunately, there are plenty of online machine learning courses to give you the needed expertise and boost your skills. This article will help you find the best machine learning course online and explain the top options in detail.

Factors to Consider When Choosing an Online Machine Learning Course

If you like the idea of online learning, machine learning courses are readily available. In fact, the number of options may be overwhelming. That’s why we’ve applied certain strict criteria when looking for the best machine learning online course. Moving forward, you should also keep those criteria in mind.

Firstly, the content of the course will matter the most. Machine learning is a broad field, and you’ll want to ensure that the education you’re getting is the one you need. Also, every genuine venue of machine learning online training should give you a solid foundation while placing a particular emphasis to specific skills.

The curriculum won’t be the only aspect of the course that matters, though. Who is teaching you will be crucial as well. Ideally, your instructor should be an experienced professional in the field so that they can teach you the theory as well as the practical applications.

Next, one of the primary reasons why you’d want to take a course rather than enroll into a BSc or MSc program is time. You don’t want a course to take up too much of your time, which is why flexibility and the overall duration are essential. You’ll want a well-structured online machine learning course that will leave room for a job or any other activities.

Beside the knowledge provided, hands-on experience will be vital. Once you complete a course, you should be able to apply everything you’ve learned there. To that end, a quality machine learning online course will focus heavily on the real-world application of the skills taught.

Finally, the pricing will play a major role. Similar to time, budgetary concerns are likely a core reason why you’re opting for a course. Simply put, you don’t want it to cost the same as a year at a university. And if the price is somewhat higher, the course should provide plenty of additional resources to justify it.

Top Online Machine Learning Courses

Imperial College Business School – Professional Certificate in Machine Learning and Artificial Intelligence

Course Overview

This program deals with the essential AI and machine learning concepts, teaching you when and how ML solutions can be applied to real-life problems. The course is relatively long, lasting for 25 weeks. It was developed in collaboration with the Imperial College’s Department of Computing.

Key Features

• Taught by experts
• Hands-on activities
• Projects worthy of your portfolio
• Ends with a capstone project
• Verified digital certificate

Pricing and Additional Resources

The price of this course is £3,995, which is reasonable considering its extended duration. During the course, you’ll have individual advisor support for career-building. Completing your studies will also grant you the status of an Associate Alumni, allowing you to join the Imperial College Business School’s community.

Google Digital Garage – Machine Learning Crash Course

Course Overview

If you want to learn machine learning fundamentals quickly and efficiently, this course is just the ticket. It includes comprehensive text and video lectures, practical exercises, and work with the TensorFlow ML library. You’ll gain relevant knowledge and experience through three modules lasting a total of 15 hours.

Key Features

• Lecturers are Google’s researchers
• Intermediate level
• Genuine case studies
• Interactive algorithm showcases
• Fast-paced and applicable

Pricing and Additional Resources

If you’re wondering how much a course from a leading tech giant company may cost, you’ll be pleasantly surprised: This Google machine learning online course is absolutely free. In addition, it’s quite short and very efficient.

IBM (via edX) – Machine Learning With Python: A Practical Introduction

Course Overview

This course teaches you supervised and unsupervised machine learning using Python. An introductory course, it may last up to five weeks. Best of all, the program is entirely self-paced, meaning you can tackle individual lessons at a tempo that suits you. It’s worth noting that this course also explores widely used models and algorithms, supported by actual examples.

Key Features

• Taught by a Senior Data Scientists at IBM
• Part of IBM’s one year certificate program for data science professionals
• Beginner-friendly
• Focus on statistics and data analysis

Pricing and Additional Resources

Like Google’s course, this program by IBM, hosted on edX, is free. It’s worth noting that there’s also a “Verified Track” version, priced on edX at $99. This version of the course will provide unlimited source material access, exams, graded assignments, and a shareable certificate.

DeepLearning.AI (via Coursera) – Unsupervised Learning, Recommenders, Reinforcement Learning

Course Overview

As a part of a specialization in machine learning, this course teaches unsupervised learning as a particular branch of ML. You’ll also learn about recommender systems and how to build certain machine learning models. The course is designed by experienced DeepLearning.AI members in collaboration with Stanford University. You’ll be able to complete it in about 27 hours.

Key Features

• Flexible course scheduling
• Part of a three-course specialization
• Taught by an experienced lecturer and ML professional
• Beginner-friendly
• Teaches specific machine learning techniques

Pricing and Additional Resources

This course, as well as the entire specialization, is available with a Coursera subscription. As a subscriber, you won’t pay any additional fees for the course. Plus, you’ll gain access to a shareable certificate, practice and graded quizzes, and other subscriber benefits.

Microsoft – Foundations of Data Science for Machine Learning

Course Overview

More than a regular course, Foundations of Data Science for Machine Learning is a learning path which consists of 14 modules. It will take you through the entire journey, from the machine learning basics to advanced architecture and data analysis. The course can be completed in under 13 hours.

Key Features

• Offered by a leading tech giant
• Provides lessons and exercises
• Entirely browser-based
• Interactive learning

Pricing and Additional Resources

This training course by Microsoft is free and available immediately. Enrolling in the course comes with no prerequisites.

Tips for Success in Online Machine Learning Courses

Once you choose a machine learning online course, simply signing up for it won’t be enough. You’ll want to ensure you’re getting the most value out of the program. To that end, it would be best to apply the following tips:

• Set your goals and expectations: The best way to get optimal results from a course is to go into it knowing precisely what you want. Clarify what you’re looking to achieve and what you expect the course to provide, and you’ll have an easier time both choosing and completing the program.
• Dedicate time to study and practice: Course lectures will be a vital part of the learning process, but the time and work you put into it will be what makes it all worthwhile. Approach your machine learning online course with the utmost dedication and responsibility, making sure to always set aside the time of day for studying.
• Engage with the community: A learning environment is perfect for building a network. You’ll contact other people with similar interests, which may broaden your viewpoint, provide additional knowledge, and even open up job opportunities. Don’t shy away from online forums or any other type of meeting place that your peers frequent.
• Try out new skills and concepts in real-life: Even if the course you pick involves practical projects, you should be proactive beyond that point. Take what you’ve learned and try to apply it on something outside the course. The best time to start practicing is as soon as you learn a new skill.
• Keep updating your knowledge and skills: Machine learning progresses rapidly, so you’ll have to do your best in keeping your knowledge and skills relevant. A quality course will give you a good foundation. However, updating what you’ve learned will be entirely up to you.

Become a Machine Learning Expert Online

If you’ve found the best machine learning course online for your purposes, you should start learning right away. Armed with the proper skills, you’ll have greater chances of getting work in the industry and starting a career in this science of the future.

Explore which machine learning online course fits you best and start pursuing your goals. You’ll find the knowledge and experience gained as the perfect catalysts for personal and professional growth.