If you’re a technical enthusiast, into programming, or simply have a passion for computers, you’ve surely already heard about deep learning. However, you don’t even need to be one to hear about it. Deep learning is everywhere, and a great example of that is just how popular Coursera deep learning courses are.
While broad and intimidating to some, this subject offers a lot to explore. Mimicking the human neural networks, therefore also known as deep neural learning, this topic is rather exciting, even to the natural science nerds.
Could you have ever imagined that the neural network could be replicated? Maybe not fully, but that computers could be based on it?
It still seems wild to me, therefore, quite fascinating. If you’re also one of the enthusiasts, don’t hesitate to take one of Coursera deep learning courses. So, the next step is deciding which one to choose.
I’m here to help you with exactly that. Looking up deep learning, you’ll be met with more than one option. Following this guide, you will know exactly which Coursera deep learning specialization or course is the most suitable for you.
So, keep on reading!
Table of Contents
- 1. What is Deep Learning?
- 2. Best Coursera Deep Learning Course
- 3. Coursera Deep Learning Specialization
- 3.1. Length of the Specialization
- 3.2. Well-Structured
- 3.3. Instructors
- 3.4. Working with Real-Life Examples
- 4. What if I Don’t Want to Take the Whole Specialization?
- 4.1. Neural Networks and Deep Learning Coursera Course
- 4.2. Improving Deep Neural Networks Coursera Course
- 4.3. Structuring Machine Learning Projects Coursera Course
- 4.4. Convolutional Neural Networks Coursera Course
- 4.5. Sequence Models Coursera Course
- 5. Conclusions
What is Deep Learning?
I couldn’t just drop you some courses without introducing what deep learning actually is. Maybe you’re a complete beginner who just heard the term and got interested in what it is. In that case, you still have a lot to learn before taking a course.
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While the courses are well-made and introduce the topic first before getting into smaller details, it’s still better to come at least knowing what you’re about to study. So, read at least the definition of it, or do some more extensive reading by checking out our article on what deep learning is.
To introduce it shortly, deep learning is closely related to machine learning and artificial intelligence. It is a machine learning system based on artificial “neurons,” which are made by taking the human neural network as an example.
So, if you want to understand it, think of machines that try to replicate human intelligence, much like artificial intelligence.
However, it’s not really the official definition. If you’d like to learn more, you should definitely do some research as deep learning is not a topic that can be defined by a sentence or two.
Just because it’s such a diverse concept, it might take a while to fully grasp it. However, you shouldn’t worry about it if you’re planning to take one of the Coursera deep learning courses. Using them, you’ll be made aware of all the needed definitions and much more.
So, if this subject still mystifies you, you shouldn’t worry at all! Just make sure you pay attention to the course material, and you’ll be good.
Now that I’ve introduced you to deep learning let’s see what Coursera has to offer. Spoiler alert: I’ll introduce some of the most popular courses: Coursera Deep Learning specialization and the courses such as Neural Networks and Deep Learning Coursera within it.
Best Coursera Deep Learning Course
Now that you are acquainted with the definition, it’s time to explore the topic more deeply. It can be done as simply as taking a Coursera deep learning course. However, before you start learning, you have to choose one course to stick to.
It might be a little hard to do. Being presented with various courses, you could get lost and not know which one is better.
While Coursera offers great quality courses, and you probably could learn from every single one of them, it’s not advisable to multi-task and learn from several courses at the same time.
Taking more than one course on the same topic? Go ahead. You’ll be sure to broaden your horizons on the topic and see different approaches! Taking a few courses at the same time? You might get lost and be unable to concentrate on what you’re learning.
Don’t take my word for it. It’s just a piece of general advice if you believe you could pull off studying from a few different courses on the topic at the same time, do it! However, most people tend to struggle. Try out what works best for you and get to studying.
Even if you choose to study from 2 courses at the same time, you’ll still need to choose them. To help you with that, I’ll introduce the best bet for you - Coursera Deep Learning specialization.
I’ve been struggling to find some nicely-written reviews of this specialization that are not biased and explain the courses thoroughly. So, I’ve realized, why not do it myself?
So, check out the deeply-researched presentations and reviews of the specialization and the courses within it!
Coursera Deep Learning Specialization
First off, let’s talk about the most popular Coursera deep learning course, or rather a specialization. If you’re not sure how a specialization differs from a simple course, let me briefly explain it to you. A specialization is a set of courses following the main topic.
Specializations are usually longer than courses and teach you about the topic way more in-depth. So, when you take a specialization, you’ll have higher chances to work with the topic and apply it in the job field.
So, let’s see what it has to offer!
Length of the Specialization
The thing that first struck me about Coursera Deep Learning specialization is the length of it. As mentioned before, specializations tend to be longer and more informative. Meaning that you’ll be able to get much more knowledge than you would get by simply taking a shorter course.
So, you get to experience the topic more deeply, and you will be able to put it into practice better. After all, the point of learning online is to get information that you can use later. The more you’re provided with, the more you’ll be able to retain and put into use.
However, what I’d like to mention here is the length of this particular specialization. It’s 4 months. To me, it seems like a perfect amount of time between a course and a specialization.
Some specializations can take up to a year to finish. As a result, many people get bored with them, dropping them halfway through, despite how much knowledge the specialization can provide. However, it’s not the case with Coursera Deep Learning specialization.
Taking 4 months to finish (if you put in approximately 5 hours of studying every week), it doesn’t appear as daunting and demotivating as the longer specializations, but it still offers enough time to learn the topic thoroughly.
I really believe they got the length of the course aspect right! So, if you believe that you can handle 4 months of active studying, make sure to check it out.
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What I really liked about this course is the structure of it. To put it simply, it starts with the basics and only then explores some of the topics that might be a little harder to grasp. It starts with the infamous Neural Networks and Deep Learning Coursera course.
This course is known for being a great introduction to the topic for those who don’t really know much about deep learning. So, starting on such a bright note, the specialization raises expectations for the other courses.
And… It doesn’t disappoint later! The first course continues to follow a logical pattern and is presented in a really nice order. Having learned the introduction part, you’ll go straight to the basics of deep learning. That means that your brain will have time to adjust to the new information.
Some courses forget that people taking them might not know much about the topic that the course is about and tend to over-complicate the course by putting the hardest part in the beginning. That often becomes demotivating, and many people quit.
However, this Coursera Deep Learning specialization doesn’t do any of that. It seems that they understand that in order to keep your attention, it has to be made easily understandable and not that hard to follow.
If you went to a language class and the first word they’d teach you would be “tantalizing” instead of teaching you how to say hello and introduce yourself, would you like that? I bet not. This information isn’t really useful for a beginner who isn’t even sure whether they want to learn a language to an advanced level.
The same goes for courses on other topics. Things should get progressively difficult over time, but it’s important to keep the beginning as simple as possible and to introduce the topic before diving into the minute details.
Fortunately, that’s exactly what the specialization does. So, if that’s what you’re looking for, don’t hesitate to check out the Coursera Deep Learning specialization.
Another great thing about this Coursera Deep Learning specialization is that it is taught by some of the best instructors.
Ever heard of Andrew Ng? He’s among the top instructors on Coursera on this and similar topics. If you haven’t checked it out yet, take a look at the Machine Learning course by Andrew Ng. He’s a real professional in his field, and students seem to love him!
I believe that it’s greatly important if the course is taught by competent instructors or not. It can make or break the course. It seems to me that people behind this specialization think the same, as all the instructors chosen are considered to be top instructors by Coursera.
Have you ever had an extremely interesting subject taught by a bad teacher in school? Well, I have, and, trust me, it reduced my interest in the subject in like 5 minutes of having to listen to the class. Just because of that, I choose to pay a lot of attention to instructors before deciding on to taking the course.
Fortunately, you won’t have to go through listening to tedious instructors rambling on to themselves for hours. These three guys, who teach this specialization, seem to have it covered, as they know how to raise and keep the attention of the audience.
So, if you’re looking for a course with top-rated instructors, definitely check out this Coursera Deep Learning specialization.
Working with Real-Life Examples
One more superb perk is that this specialization offers to work with real-life materials. Did you know that by taking this course, you can learn to apply deep learning to sign language? Sounds really exciting to me.
Also, not only sign language, but also music generation, healthcare, autonomous driving, and natural language processing.
Having worked with real-life data, you will have acquired some invaluable knowledge. While working with resources gathered specifically for learning purposes works just as well, working with real-life resources provides some excitement and gives you tangible results.
It will also be more motivating for you. Remembering school days and countless exercises that don’t resemble real life at all (think of things like “a man bought 15 watermelons for $0.36/lb…”), not that motivating when you can’t really think of buying 15 watermelons yourself.
Well, taking this course, you won’t have to deal with any of that. Working with real-life examples, you’ll be able to concentrate better and stay motivated as you’ll see the actual results of your work. Great, isn’t it?
Also, it looks better in your portfolio. Having real results in a sphere where deep learning can be applied provides some eligibility for your portfolio. So, if you’re looking to build it up, this is a great chance!
What if I Don’t Want to Take the Whole Specialization?
I understand that for some, it may seem to be a little too big of a commitment. Spending 4 months on learning is a long time if you’re not looking to apply it to the job sphere. What if you only want to learn more about it to understand the main principles?
Well, there are great alternatives to taking the whole specialization. However, simply because it’s so well-made, I’d advise you to stick to the courses of the specialization, but don’t take it whole. Choose what you would like to learn and what you would like to stay away from.
My best suggestion is to take the Neural Networks and Deep Learning Coursera course. It’s the first course of the specialization, and it works as a great introduction to the deep learning world. So, if you don’t have as much time to spare but still are interested, this would be the best option.
Either way, let’s look into all the courses of this specialization to help you choose.
Neural Networks and Deep Learning Coursera Course
As I have briefly introduced it already, this course is your gateway to the deep learning world. If you have zero experience and zero knowledge of the topic, don’t panic! The course will provide you with all you could need when learning about deep learning.
The course starts with an introduction to deep learning. While it seems self-explanatory, there are so many courses that fail to introduce the topic for those who have no idea what they’re about to learn, but fortunately, this course has it covered.
Not only that, but this course seems to be very well-made overall. Although no less is expected from Andrew Ng, he’s known for having courses of extremely high quality.
What’s the best thing for those who are looking to save some time and still learn about deep learning is that this Neural Networks and Deep Learning Coursera course only takes 20 hours to complete.
Even if you were the busiest person on earth, you could still dedicate a few weeks to spread these hours in, couldn’t you? I’m sure you could!
So, if all those things are what you’re looking for in a course, don’t hesitate to check out Neural Networks and Deep Learning Coursera course.
Improving Deep Neural Networks Coursera Course
The course that follows after the Neural Networks and Deep Learning Coursera course in this specialization is the Improving Deep Neural Networks course. Yet another rather short course that will provide the basis of deep learning for you.
In these approximately 20 hours that will take to complete, you’ll build an even stronger basis for furthering your deep learning knowledge and also start learning more about the intricacies of the subject.
Just by taking this course, you’ll be able to build your own neural network and learn to train it. Exciting, isn’t it? However, the benefits don’t end here.
One major pro that I found is that it prepares you for job interviews in AI. That means that the preparation you’ll need before applying for a job position will be reduced drastically. It’s always good to come to an interview prepared to answer the most common questions.
If, however, you’re not interested in a job in this sphere but like programming and might consider applying for a job, let’s say in Python programming, check out our guide on the most common Python interview questions. If you’re looking for other programming or data science jobs, check out all of our articles, and maybe you’ll find what you’re looking for.
Structuring Machine Learning Projects Coursera Course
The third course in the Coursera Deep Learning specialization is the Structuring Machine Learning Projects course. If you were worried that this specialization lacks hands-on experience, this course will definitely prove you wrong.
This course concentrates on projects and gives you practical experience. Also, it’s important to note that Andrew Ng promises that you’ll find information that hasn’t been taught before in any of his courses. The course is taught from his own experience, meaning that you’ll get industry insider’s information.
It is also mentioned that this information is crucial if you’re looking to get a job in the industry. It is stated that this information saves a lot of time when working on deep learning projects. So if you’re looking to do deep learning as a career, pay special attention to this short course.
Convolutional Neural Networks Coursera Course
Have you ever wondered how they create facial recognition systems? How is it possible to make self-driving cars? Well, it will all be explained in this Coursera Deep Learning specialization course.
The specialization goes way more in-depth with this course, compared to the first courses in the specialization, meaning that it might be a bit harder to take if you’re not very familiar with the topic of deep learning.
However, this is a great practical course that provides invaluable insights to those who already have some knowledge in the field. So, if you’re an intermediate or an advanced learner, looking to take a course that would deepen your skills, this course might be it.
Sequence Models Coursera Course
The last course in the Coursera Deep Learning specialization is the Sequence Models course. This one is quite similar to the previous one. However, it mostly concentrates on sequence data like natural language or audio.
It also teaches the practical skills of how to create models. Having taken this course, you will be able to build speech recognition systems, machine translation, natural language understanding, and many more.
So, if you already have some knowledge in deep learning, you’re welcome to skip the introductory course and concentrate on the courses that offer more specific knowledge, like this one. However, I would still advise you to check out as many courses as possible to deepen your knowledge.
Coursera Deep Learning specialization is among the top courses on Coursera. You might be thinking: what makes it so special? Well, there are a lot of reasons why people choose to take this particular specialization.
The Coursera Deep Learning specialization is very well made, it’s not too long, it’s well-structured, it has only top-rated instructors, and it also offers working with real-life examples, meaning that you likely won’t get bored when taking this specialization.
However, taking the whole specialization is not the only option. You may also just take one or a few courses from it. Doing that is mostly recommended to intermediate or advanced learners who already have some experience but want to deepen their knowledge.
So, if this article convinced you, make sure to head to Coursera and check out the Coursera Deep Learning specialization.