Discover the best courses in Computer Science, Data Science, Machine Learning & AI. Find top-notch educational resources to enhance your skills.
Search inside The Magazine
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.
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.
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.
- 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
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.
- 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
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.
- 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
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.
- Taught by a Senior Data Scientists at IBM
- Part of IBM’s one year certificate program for data science professionals
- 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
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.
- Flexible course scheduling
- Part of a three-course specialization
- Taught by an experienced lecturer and ML professional
- 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
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.
- 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.
As the world becomes increasingly data-driven and computing power advances beyond all expectations, two intriguing fields are at the center of attention – data science and machine learning.
These fields are often grouped together as they have numerous contact points. First and foremost, both areas are all about data. But data science primarily focuses on extracting valuable insights from data, while machine learning aims to use the data to make predictions and decisions without explicit programming.
These revolutionary technologies have seeped into (and revolutionized) virtually every existing sector: healthcare, business, finance, retail, IT, and the list can go on and on. So, no wonder companies are constantly seeking highly skilled professionals in these fields.
If you’d like to build a career in these highly lucrative fields, improving your skills and knowledge is an absolute must.
Luckily, nowadays, you don’t have to leave your home to achieve this level of expertise. Just pick a data science and machine learning course from this list (or do all three!), and you’ll be well on your way toward a bright future in these burgeoning fields.
Top Data Science and Machine Learning Courses
Whether you’ve just started to dip your toes in these fields or want to take your skills to the next level, you’ll find the perfect data science and machine learning course on our list.
Data Science: Machine Learning by Harvard University
The first data science and machine learning course on the list is classified as an introductory course. In other words, it’s ideal for beginners.
The course first tackles the basics of machine learning, gradually digging deeper into popular algorithms, principal component analysis, and building recommendation systems. You’ll finish this course with fundamental data science and machine learning skills.
The class lasts eight weeks and is entirely self-paced. The recommended time commitment is two to four hours per week, but every learner can tailor it to their needs. Another great option is auditing this data science and machine learning course for free. But you’ll have to pay a fee for a verified certificate and unlimited access to the materials.
The $109 (a little over €101) cost is a small price for the theoretical and hands-on knowledge you’ll gain after this course.
Unfortunately, not everyone will be given a chance to gain this knowledge. Due to some licensing issues, this course isn’t available for learners in Iran, Cuba, and Ukraine (the Crimea region). Another potential downside is that the class is a section of a nine-part data science program. And most of those nine parts precede this course. Although not obligatory, the program creators recommend taking these courses in order, which can be too much time and financial commitment for some learners.
Machine Learning, Data Science, and Deep Learning With Python by Udemy
Do you feel like you need more hands-on experience in machine learning and data science? Have you had to pass on promising job applications because you don’t meet the listing requirements? If you’ve answered positively to both questions, here’s some good news. This data science and machine learning course was custom-made for you.
And no, these aren’t empty promises à-la infomercials you see on TV. This course covers all the most common requirements big-tech companies seek in data scientist job listings. Implementing machine learning at a massive scale, making predictions, visualizing data, classifying images and data — you name it, this course will teach it.
Naturally, this is the single most considerable advantage of this course. It will give you the necessary skills to successfully navigate the lucrative career paths of data science and machine learning. But this only goes if you already have some experience with coding and scripting. Unfortunately, this course isn’t beginner-friendly (in terms of Python, not data science), so not everyone can take it immediately.
Those who do will enjoy over 100 on-demand video lectures, followed by several additional resources. For a $119.99 (approximately €112) fee, you’ll also receive a shareable certificate and full lifetime access to the course.
Data Science and Machine Learning: Making Data-Driven Decisions by MIT
The last item on our list is a big-league data science and machine learning course. The word “course” might even be an understatement, as it’s closer to an entire learning program encompassing a broad set of educational activities.
For starters, the course involves a mentorship program with leading industry experts as guides. And this isn’t a one-and-done type of program either; you’ll have weekly online meetings in small groups. The course itself is taught by MIT faculty and industry experts with years of experience under their belts.
In 12 weeks, you’ll significantly grow your data science and machine learning portfolio, examine numerous case studies, acquire valuable knowledge in applying multiple skills (clustering, regression, classification, etc.), and receive a professional certificate to prove it.
The only notable downside of this extensive data science and machine learning course is its price. With a $2,300 (around €2,142) fee, this course is far from accessible for an average learner. However, those who can afford it should consider it a long-term investment, as this course can be a one-way ticket to a successful career in data science and machine learning.
Factors to Consider When Choosing a Course
Online learning platforms have democratized the world of learning. Now, you can learn whatever you want from wherever you are and at whatever pace works best for you.
But keep in mind that this goes for instructors as well. Anyone can now teach anything. To avoid wasting your time and money on a subpar course, consider these factors when choosing the perfect data science and machine learning course.
Course Content and Curriculum
First things first: check what the course is about. The course’s description will usually contain a “Curriculum” section where you can clearly see whether it delves into topics that interest you. If you have experience in the field, you’ll immediately know if the course spends too much time on skills you’ve already mastered.
Course Duration and Flexibility
Most online courses are self-paced. Sure, this kind of flexibility is mostly a good thing. But if you lack discipline, it can also be detrimental. So, before starting the course, check its duration and make sure you can fully commit to it from beginning to end.
Instructor Quality and Expertise
A data science and machine learning course will undoubtedly contain portions some learners might perceive as challenging or tedious. If there’s one thing that can help them breeze through these parts, it’s an engaging and personable instructor.
So, before committing to a course, research the instructor(s) a little bit. Check their bios and play a video to ensure their teaching style works for you.
Cost and Return on Investment
A data science and machine learning course can cost upwards of thousands of dollars. To ensure you’ll get your money’s worth, check how well it will prepare you for finding a job in the field.
Does it come with a highly requested certification? Does it cover the skills your future employers seek? These are just some of the questions you should consider before investing in a data science and machine learning course.
Hands-On Experience and Real-World Projects
This is another factor that can make investing in a data science and machine learning course well worth it. As valuable as theory is, hands-on experience is king in these fields. Working on real-world projects and building a rock-solid portfolio opens up new doors for you, even before leaving the course.
Networking Opportunities and Job Placement Assistance
A strong support system and direct contact with instructors and mentors should be a course must-have for anyone interested in a data science and machine learning career. Meet notable figures in the industry and stand out among the course goers, and incredible job opportunities should follow suit.
Tips for Success in Data Science and Machine Learning Courses
You can get straight to learning after selecting the perfect data science and machine learning course. Sure, closely following the curriculum will help you gain the necessary knowledge and skills in these fields. But following these tips while studying will do wonders for your future career prospects:
- Develop a strong foundation in mathematics and programming: This will allow you to take more advanced courses and breeze through the rest.
- Stay up-to-date with industry trends and advancements: Despite being updated frequently, the courses can barely keep up with the innovations in the field.
- Engage in online forums and communities for support and networking: Sharing ideas and receiving feedback can help you overcome learning challenges.
- Practice your skills through personal projects and competitions: Challenge yourself to go beyond the scope of the course.
- Seek internships and job opportunities to gain real-world experience: Besides looking great on your resume, these will help you get the hang out of things much quicker.
Learn, Practice, Excel
A carefully selected data science and machine learning course is an excellent opportunity to enter these booming fields with a bang. Developing data science and machine learning skills further will help you stay there and enjoy a successful and rewarding career for years to come.
You’d be hard-pressed to find a more rapidly evolving industry than computer science today. It seems like new solutions and applications in the field pop up every day, and the market has never been hungrier for talent.
If you’re interested in computer science, you’re in luck. This industry has some of the best-paid jobs worldwide and is full of exciting developments and novel challenges. Of course, many people are competing for those work positions, which is why you must do everything in your power to gain an advantage.
With an online masters computer science program, you can get the edge over other candidates in the market. Completing such a program will provide you with cutting-edge knowledge and equip you with the most relevant skills. In other words, an online MSc computer science program may help you start a career in the field.
But choosing the best online masters computer science program may be more complex than it seems. Numerous institutions offer this type of education, so finding the top options among the crowd could be a challenge.
This article will serve as your complete guide to online masters computer science programs. We’ll explain what to look for, recommend some of the leading options, tell you how to apply, and outline what awaits once you graduate.
Factors to Consider When Choosing an Online Masters in Computer Science Program
It goes without saying that you shouldn’t enroll in a master’s program without closely examining your options. Be sure to pay attention to specific criteria when considering where you’ll get your online MSc computer science degree:
- Accreditation: You’ll want to graduate at an accredited institution with degrees that are recognized worldwide. Other universities and employers won’t consider unaccredited degrees particularly valuable or trustworthy. Plus, accreditation serves as proof that an educational institution meets certain international standards.
- Curriculum and specializations: Your choice of an online masters computer science program will depend on the classes provided. The curriculum should be appropriate for your purposes and goals. And since computer science is a broad field, you’ll want to ensure the program has the right specialization options for you.
- Faculty expertise: A master’s program will often be only as efficient as the people teaching it. Faculty members at your program of choice should, of course, be experts. They should also have extensive experience to provide practical guidance and show you how computer science is applied in real-life.
- Program format and flexibility: Online programs have the major advantage of not requiring you to physically attend classes. This means that a certain level of flexibility is expected, both in terms of day-to-day lectures and deadlines. The flexibility principle often extends to the curriculum itself, with many programs offering a large number of electives.
- Tuition fees and financial aid: Considering the tuition price is always worthwhile. Like everything else in the market, master’s programs can be under- or overpriced. You should take care that you’re getting the right value for a reasonable sum. Plus, there are usually financial aid options available to help soften the financial impact.
- Student support services: Lastly, the best online masters computer science programs will offer extensive support to students. This can represent a massive benefit when you need counseling or extra guidance. Even better, your program might include career support, nurturing you from student to graduate to employee.
Top Online Masters in Computer Science Programs
1. International University of Applied Sciences (IU) – Master’s in Computer Science
This program offers practical education in computer science. Focusing mostly on artificial intelligence, cybersecurity, and data science, it lasts between two and four years, depending on whether you study full or part-time.
- Dual degree option available
- Accredited according to European standards
- Entirely flexible
To enter this program, you’ll need to provide a computer science-focused undergraduate degree from a recognized institution. English proficiency will also be mandatory. The monthly tuition fee is €278 for full-time, €209 for part-time (three years), and €165 for part-time (four years) students.
2. University of Essex – MSc Computer Science
This two-year program by the University of Essex starts by examining the theoretical fundamentals. Then, it allows you to choose a specific field of focus and study it in detail through lectures and practical applications.
- BCS accreditation
- Tuition fee covers learning tools like programming languages
- Suitable for students from a different background
You can apply for this program with an undergraduate degree or if you’ve worked in a relevant field for a minimum of three years. English language qualification is acceptable from IELTS or a similar school. If lacking such qualifications, the University of Essex also lets you take a free online test. The tuition fees are £12,167 for UK and £12,428 for international students with possibilities of scholarships and discounts.
3. MIA Digital University – Master in Computer Science – Cybersecurity, Data Analytics, and Artificial Intelligence
Based in Barcelona, Spain, the MIA Digital University offers a computer science program that tackles some of the most requested profiles in the industry. You’ll learn about the latest developments in cybersecurity, data analytics, and AI, as well as how to apply them in practice. The program lasts for one year.
- Dual degree with Universidad a Distancia de Madrid (Udima)
- Student internships offered
- Heavily project-based
Application for this program will require a previous degree, which doesn’t have to be from the computer science field. You’ll also need to submit a resume or CV and a valid ID. The price of the program is €3,900, with scholarships available.
4. BTH Sweden – Master’s Program in Software Engineering
Somewhat more specific than other programs on the list, this MSc focuses on software engineering. However, the program also leans heavily into data science, machine learning, and AI. For that reason, you may view it as a computer science program with an emphasis on software engineering. The program is two years long.
- Mixed-time structure
- Work in groups and individually
- Based on leading software engineering research
To apply for this program, you’ll need a BSc degree in Engineering with a minimum 15-credit degree project. Professional experience of no less than two years in software development is also required, preferably with programming involved. The program doesn’t have a tuition fee for UK students, while others will need to pay SEK 60,000 for each semester.
How to Apply for an Online Masters in Computer Science Program
Precisely how you apply for an online masters computer science program will depend on the institution. There’s no universal application process, but you can keep certain guidelines in mind.
First, get detailed information about the requirements. Most master’s programs will require previous education in the field, although some may accept provable work experience instead of a degree.
Certain programs will also ask for a letter of recommendation and statement of purpose. But even if these documents aren’t requested, including them in your application will usually be a plus. Also, online MSc computer science programs are commonly held in English, so you’ll need a level of language proficiency and the appropriate certification.
Learning all of the relevant information in time and getting your documents in order will be pivotal. The last thing you’d want to do is miss out on a program by submitting an incomplete application. Additionally, you’ll have the greatest chances of success if you apply for a program with requirements you’re certain you’ll meet.
Every program will have specified deadlines and dates for application, interviews, covering the fees, and enrollment. Naturally, missing those dates would likely result in you not being accepted, so do your best to stay on track.
Career Prospects for Graduates of Online Masters in Computer Science Programs
Computer science graduates have plenty of options in the job market. Experts in this field are needed in various industries, including finance, IT, healthcare, and commerce.
Depending on your specialization, you could work in programming, database management, cybersecurity, robotics, network engineering, etc. The average base yearly salary for a computer science MSc graduate is €56,000. Of course, this will vary widely depending on your field of expertise, industry, and experience.
Your online masters computer science degree will also allow you to continue your education. You can move forward to different specializations, either in a particular field or interdisciplinary. In addition, an MSc may make you eligible for a PhD program, if you’re interested in further academic progress.
Complete an Online MSc Computer Science Program and Start a Rewarding Career
Choosing an online MSc computer science program that fits your goals can be an immensely valuable career and educational move. This degree will give you an advantage in the job market and help you hone your professional skills. Plus, enrolling in a postgraduate program will create networking opportunities that may be of great importance.
If getting an MSc in computer science sounds enticing, there’s no reason not to start working on it right now. Research the programs that suit your needs and don’t hesitate to apply. You’ll be making a worthwhile step in the right direction.
When artificial intelligence (AI) first emerged, it was perceived as nothing more than a gimmick, an exciting sci-fi idea with no practical applications. It took a few decades to dispel these misconceptions. Still, considering the importance of AI today, they’re definitely ancient history.
Since AI aims to simulate human intelligence processes like learning, reasoning, and creativity, it has found its way into numerous industries that rely on these skills to prosper. Healthcare, retail, security, and finance are just some industries that have experienced the benefits of AI firsthand.
As AI permeates more and more of everyone’s daily lives, the need for highly skilled AI professionals is only growing. And if you are to take on a new career, AI is the way to go. This lucrative field offers seemingly endless job opportunities and a unique chance to shape the world’s future.
If you’ve been eyeing the AI career path for a while, an AI certification course can help you get the hang out of the basics and enter this field with a bang. Even if you have experience with AI, there’s always something new to learn.
Whatever the case, you’ll learn something valuable from each AI certificate course on this best-of list.
Benefits of AI Certification Courses
An AI certification course is an excellent way to immerse yourself in this technology and earn a helpful certificate in the processes. And that’s only the beginning. Check out some of the most appealing benefits of completing one of these courses.
Enhancing Career Prospects
Considering the ever-growing power of AI, it isn’t surprising that your prospective employers are some of the biggest tech companies and market disruptors. Google, Amazon, Microsoft, and Apple are just some tech giants looking for employees well-versed in AI.
On top of that, getting certified in AI opens up a world of possibilities in terms of job prospects. Sure, you can be an AI engineer. But with these skills, you can also pursue a career as a data scientist, software engineer, machine learning engineer, and more.
Staying on Top of the Latest AI Trends
The AI field is constantly up to something new. Just when you think you’ve got it all figured out, a new AI craze appears and takes the world by storm. Taking the latest AI certificate course will allow you to stay on top of these trends and even stay ahead of them.
Gaining a Competitive Edge in the Job Market
The demand for AI doesn’t show any signs of slowing down. As people catch on, the field gets increasingly crowded by those seeking a sizeable paycheck. But being self-taught and getting certified in the field are worlds apart.
With an AI certification course under your belt, your career prospects will look much better. Potential employers will perceive you as a worthy candidate from the get-go. Throw some hands-on experience into the mix, and your competitive edge will be off the charts.
Improve Problem-Solving and Decision-Making Skills
AI is all about tackling complex cognitive processes, such as problem-solving and decision-making. So, through learning the AI methodology, you’ll also work on these skills. And the best part is that these skills can benefit you in solving real-life problems and in other fields far beyond AI.
Top AI Certification Courses
If you’re keen on taking an AI course, you’ll have many choices online. Just search the words “AI certificate course” and see for yourself. However, only some courses you encounter will help you achieve your goals. To help you avoid wasting time and money, here are the top three AI certification courses and all the necessary information about them.
1 – IBM Applied AI Professional Certificate
If you’re new to AI, this is the AI certificate course for you. This beginner-friendly program will ease you into the world of AI, teaching you all the terms you’ll need to navigate this field.
But don’t worry, that’s just the beginning. Afterward, you’ll dive into the practical portion of the course and learn how to build AI-powered tools, create virtual assistants, and apply computer vision techniques.
During this program, you’ll explore the following concepts and tools:
- Data science
- Machine learning
- Natural language processing
- Image classification and processing
- IBM Watson AI services
At a pace of 10 hours a week, you’ll need about three months to complete this AI certificate course. Plus, you’re free to adjust this schedule, as the course is entirely self-paced.
As for the fee, you can use Coursera’s free seven-day trial to start. Once those seven days are up, you’ll be charged $39 (a little over €36) monthly to continue studying.
Complete the program, and you’ll earn an employer-recognized certificate from IBM demonstrating your technical proficiency in AI.
2 – Artificial Intelligence A-Z
You might be interested in this AI certificate course if you already have some basic Python knowledge. You’ll start with fundamental AI concepts but quickly move on to hands-on experiences. Learning how to make a virtual self-driving car, creating an AI to beat games, and solving real-world problems with AI are just some practical skills you’ll learn here.
As the name implies, this course will take you from a beginner to an expert in specific AI skills. To achieve this, you’ll need to go through 17 hours of on-demand video lessons, 20 articles, and three additional resources.
For a $99.99 fee (a little over €93), you’ll gain lifetime access to this course’s contents and receive a shareable certificate.
3 – Artificial Intelligence Engineer (AIE) Certification
Learners wanting to earn official certification in the AI field should look no further than this AI certification course. This course’s tagline is “The Qualification that Matters,” and it’s entirely true. After all, this course and the ensuing certification exam are offered by the Artificial Intelligence Board of America (ARTiBA), the world’s leading AI certification body.
This AI certification course functions differently than other courses on our list. The main difference is that you take a certification test after completing the learning portion.
The curriculum for this course includes the following topics:
- Machine learning
- Supervised and unsupervised learning
- Reinforced learning
- Neural networks
- Natural language processing
- Cognitive computing
- Deep learning
As you can see, this AI certification course leaves no stone unturned. But don’t let the complexity of the course scare you. Think of it as a path to acquiring highly sought-after skills and job-ready capabilities that will propel your career in AI forward.
The entire program costs $550 (close to €513). Once you pay the fee and register, you’ll have 180 days to master the learning materials and prepare for the AIE certification exam.
Factors to Consider When Choosing an AI Certification Course
Exploring more AI courses beyond these top picks may seem enticing. But before you make a final decision, consider these factors when choosing your next AI certificate course.
Course Content and Relevance
Before starting your search, take some time to assess your current career goals. What AI field interests you the most? What skills do you lack for your dream job? Think of these and similar questions and clearly define what you want to get out of the AI certificate course.
Once you do this, it’s only a matter of determining whether the course’s curriculum is relevant to your career path. Check the course’s description and see if it covers the topics you’re interested in. If it does, it passes the first elimination round.
Course Duration and Flexibility
The next factor is how well your chosen AI certificate course fits your lifestyle. If you’re a student, great; you probably have more wiggle room in your schedule. But you’ll have to find something more flexible if you’re already working and looking to switch fields or improve your AI skills.
The course’s description will also help you in this regard. Check how long the course lasts, whether it’s self-paced, and how much time you must devote to it weekly. Only start the course if you can fully commit to it.
Course Provider’s Reputation and Industry Recognition
As important as the course’s content is, ensuring it comes from a reputable organization is also crucial. Universities like MIT and Harvard are a great way to go. Of course, you should also consider recognized names in the AI industry (Google, IBM, Microsoft, etc.)
Sure, an AI certification course from these institutions looks better on your resume. But you can also rest assured that the content you’ll learn is high-quality, accurate, and up-to-date.
Cost and Return on Investment
You can find plenty of free AI courses on the internet. But if you want the best of the best (and receive a certificate at the end), be prepared to pay a course fee. Take one look at these fees online, and you’ll see prices ranging from €30 to thousands of euros.
But be careful, as the more expensive courses aren’t necessarily better. What makes a high price tag worth it is a whole set of course features. So before paying any fee, research whether the knowledge, support, and certificate you’ll receive will secure many job opportunities in the future.
Master AI and Transform Your Future
With a high-quality AI certification course under your belt, there’s no stopping you in the computer science field. Choose your courses wisely, and you’ll always stay ahead of the competition in the job market.
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on enabling computers to “think” for themselves. Of course, they owe this thinking to humans (data scientists and ML engineers) who continuously supervise ML algorithms and models.
So, there’s no AI takeover (for now at least), just incredible ways to propel several industries forward by automating repetitive tasks, extracting valuable insights from data, and improving decision-making processes.
But how do humans precisely communicate with computers in machine learning?
The answer is through programming languages.
One programming language stands out among the rest for its simplicity and versatility. By the title of this guide, you can already guess we’re talking about Python.
This beloved programming language is all over the machine learning field, so mastering it gives you a great head start in the industry.
With this in mind, let’s examine how you can learn Python for machine learning courses. If you already have some basic knowledge of this programming language, don’t worry. We’ll also mention a great machine learning Python course to take your knowledge to the next level.
Factors to Consider When Choosing a Python for ML Course
Do a Google search for “machine learning Python course,” and you’ll be met with dozens of web pages that promise a sound understanding of this programming language. However, you’ll find the best course for your needs if you can identify those needs first.
Course Content and Curriculum
Your chosen course’s curriculum is arguably the most important factor for selecting the perfect machine learning Python course. One look at the listed topics, and you’ll know whether the course is right for you.
Let’s take your previous experience with Python as an example. If you have none, a course that jumps straight into machine learning algorithms without covering the Python basics will obviously not work for you.
Instructor’s Expertise and Experience
What bridges the gap between struggling to comprehend a complex subject and feeling that nothing can stop you in your learning journey? The answer is simple – a good instructor.
Before committing to a course, check who teaches it. Find out the instructor’s background with Python and whether they have enough expertise to guide you through this programming language’s intricacies.
If their bio checks all the boxes, watch at least one of their lectures. It doesn’t hurt to check whether their teaching style and voice suit you, as these can also make or break your learning experience.
Course Duration and Flexibility
Most online courses are self-paced, allowing you to create your own schedule. Fixed-timing courses also have their benefits, though. They’re usually instructor-led, so you can use the opportunity to ask questions and receive clarification as you learn the material.
As for duration, the course’s description typically indicates how long the course lasts and the recommended pace. Before starting, make sure you can commit to the course from beginning to end. Otherwise, you’re just wasting time and gaining incomplete knowledge.
Hands-On Projects and Real-World Applications
Programming languages are inherently practical, so ensure that your chosen course features hands-on projects and practical examples. Sticking solely to theory will do little to prepare you for what’s waiting in the real world.
Course Reviews and Ratings
You probably check reviews before going to a new restaurant, renting an Airbnb, or purchasing clothes online. So why should shopping for online courses be any different? When a course piques your interest, check how other learners have rated it. But don’t stop at glancing at the average rating. Read through some reviews to ensure they aren’t fake and to get a better picture of the course’s quality.
Pricing and Value for Money
There are plenty of free machine learning resources online. But the more advanced courses and certificates usually come with a fee. And that’s perfectly understandable. What’s not understandable or acceptable are courses that charge ridiculously high fees yet offer little value. To avoid wasting money (and probably time), check whether the course’s price is justifiable by its duration, level, type, and provided support.
Top Python for ML Courses Reviewed
Here are our favorite Python courses primarily focused on machine learning. We’re positive you’ll find the perfect machine learning Python course, whether this is the first time you use this programming language or want to master this skill.
Python for Machine Learning
The Python for Machine Learning course on Great Learning is a great place to start your Python-learning journey. This course is beginner-friendly and relatively short, so you won’t get overwhelmed from the get-go.
This course focuses on three Python libraries: NumPy, Pandas, and Matplotlib. It guides you through the basic concepts (arrays, intersection, loading, etc.) and then moves on to more complex functions. At the end of the course, you take a quiz. Pass the quiz, and you’ll get a certificate of completion.
Applying for this course is free. Not only that, but you’ll also receive free lifetime access, so you can revisit the course whenever you’d like. Although, some learners believe that there’s little to revisit. In total, this course lasts for 90 minutes. Those who are serious about Python learning will probably need more than this.
Still, you can view this course as a beginner’s guide and move to more advanced lessons afterward. To apply, you only need to create an account on the platform and send an enrollment request.
Machine Learning A-Z: AI, Python & R
If you want to start with the basics but cover the more advanced stuff within the same course, this Udemy’s gem is for you. It covers another programming language besides Python, R. However, this won’t be an issue, as you can focus solely on Python.
The course is broken into 10 parts, with over 40 hours of on-demand videos. Each section (and even the lessons within them) is separate, so you can choose to complete the ones that will benefit you now. Start with data preprocessing, and work toward machine learning model selection.
Those seeking practical exercises in Python will love this course. However, you might need to research some notions independently, as not all lecture sections are explained in great detail.
You can purchase lifetime access to this course for $89.99 (a little over €83). The price includes a certificate of completion and several additional learning materials (articles and downloadable resources). Complete the purchase to apply for this course.
Machine Learning With Python by IBM
IBM is one of the leading companies in the machine learning field, so you should take advantage of every chance to learn from its experts. If you’re just gaining your footing in machine learning, you’ll cover all your bases with this offering.
It will take approximately 12 hours over four weeks to complete the coursework. After each lesson, you’ll get a chance to put your newly-learned knowledge to the test.
One thing to keep in mind is that this course focuses more on machine learning using Python than the programming language itself. So, if you’ve never worked with Python, an additional resource or two might come in handy.
You can use Coursera’s 7-day trial to enroll in this course. Afterward, you’ll be charged $39 (approximately €36) a month. The same fee is a must if you want to receive a certificate.
The Complete Machine Learning Course With Python
Are you a data scientist in the making looking to build a solid portfolio with Python? If yes, you’ll love this course. You can find it on Udemy, just like millions of learners before you. This number might surprise you at first. But once you see that one of the founders of this course is Andrew Ng, a thought leader in machine learning, it will make much more sense.
In 18 hours, this course covers all the basics of machine learning with Python. But there’s a catch. You’ll need at least basic Python programming knowledge to keep up.
If this isn’t an issue, create an Udemy account and pay the $59.99 (around €55.50) fee to apply. Lifetime access and a certificate of completion are included.
Programming for Everybody (Getting Started With Python)
While not focused on machine learning per se, this course is necessary for anyone who has yet to work with Python. Pair it with one of the other courses on our list, and your success is guaranteed.
As the name implies, this course covers all the basics. It is designed to allow virtually anyone to follow, regardless of their skills. The simplest math is all you need.
You’ll also need 19 hours to complete this course offered by the University of Michigan. However, the instructor snuck a couple of non-Python-related stories into those 19 hours, which some learners didn’t like.
If you don’t mind a break here and there, join this course on Coursera for free or $49 (a little over €45) if you want a certificate.
Additional Resources for Learning Python for Machine Learning
Perhaps you can’t get enough of learning about Python. Or you find Python for machine learning courses lacking information. Whatever the case, you can find additional resources (both online and offline) to help you master this programming language. Check out some of our favorites:
- Books and e-books: “Python for Data Science, for Dummies,” “Introduction to Machine Learning with Python: A Guide for Data Scientists,” “Python Data Science Handbook: Essential Tools for Working with Data”
- Blogs: Planet Python, Real Python
- YouTube channels: IBM Technology, Google Career Certificates, techTFQ
- Community forums and discussion groups: Kaggle Discussions, Reddit (r/learnpython)
The Path to Python
As you can see, there’s no shortage of Python for machine learning courses, even hosted by some of the biggest names in the industry. Take one of the listed courses or combine them; the choice is all yours. All that matters is that you ultimately master this programming language and crush any data science career you choose.
If these courses aren’t enough to quench your thirst for knowledge, a Bachelor’s in Modern Computer Science will definitely do the trick. With it, you can learn all the ins and outs of Python and machine learning in general.