Since its launch in 2017, Coursera’s Deep Learning Specialization has been one of the most popular deep learning programs in the world with over half-million enrolled learners. It’s suitable for beginners and experts, especially those who want to take their Machine Learning skills to the next level.
The Instructor The specialization instructor is known as Andrew Ng, and he is the Co-founder o this online platform, Coursera, and a businessman and an investor. He earned his bachelor’s degree in computer science and statistic back in 1997 from Carnegie Mellon University and a master’s degree from MIT next year.
This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.
Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning.
In short, Coursera's Deep Learning Specialization is comprehensive, engaging, informative, and up-to-date which makes it really worth it.
The foundational concepts of machine learning will never be out dated.
The Deep Learning Specialization consists of five courses. At the rate of 5 hours a week, it typically takes 5 weeks to complete each course except course 3, which takes about 4 weeks.
Are Coursera Certificates worth it? On the whole, yes. If you're seeking promotion, looking for a career change, or the skills you are learning are highly sought after, then a Coursera Certificate is definitely worth the investment. Coursera partners and course providers are world class.
To find courses in the old stack that you had signed up for before, visit the My Courses section on Coursera. The old platform courses will be present in Archived tab along with other courses.
Stanford's Machine Learning course taught by Andrew Ng was released in 2011. 8 years after publication, Andrew Ng's course is still ranked as one of the top machine learning courses.
I did it one hour a day, six days a week, and it took me six months. I did take good notes and sometimes I would go through the material twice to make sure I understood it.
It is quite possible to learn, follow and contribute to state-of-art work in deep learning in about 6 months' time. This article details out the steps to achieve that. - You have some programming skills. You should be comfortable to pick up Python along the way.
After Covid-19 pandemic and closure of offline classes in Institutes, many Institutes collaborated with Coursera and Its starts giving one of the any course for free of cost, moreover you will get a certificate also.
Courses on both platforms are vetted by industry experts and are frequently updated and reviewed for quality. However, edX marginally edges out Coursera in terms of quality. When sampling a wide range of courses on both platforms, you'll find edX courses to be better designed to impact value.
Unless there is a specific reason not to, you should list Coursera credentials in your Education section. One reason why you might want to make an exception is if your previously earned academic credentials are in a field unrelated to the role you're pursuing.
This means that Coursera certificates are indeed legitimate and accredited by leading universities. Some of these universities are incredibly well-known and respected, such as Stanford, Berklee, Duke, Yale, and many more. There are over 200 universities partnered with Coursera.
Now, let’s start to find out whether the Deep Learning Specialization by Andrew Ng on Coursera is the right course to learn Deep Learning or not. We’ll review the course on important parameters like Instructor, course content, and what other people who have already taken this course think about this specialization.
This specialization will deep dive you into the Artificial Intelligence industry, and you will master not only the theory behind this science but also applied in real-world projects, so let’s see what this course offers:
In the previous post, I reviewed one of the most successful intro courses on Machine Learning. Namely, “Machine Learning” by Andrew Ng on Coursera.
You will start with a gentle intro to Neural Network architectures – the main driving force behind them, what problems they solve, and in what domain areas they are utilized.
This Specialization is not for the absolute beginner in Machine Learning.
From my experience, it takes around 5-6 months to complete the Specialization, spending 5-7 hours per week.
With this Specialization you get a 7 day free trial and then it’s $49/month.
In this section, I’ll provide a deeper overview of each of the courses in the Specialization.
In this section, I will share some of the most common negative feedback from my experience and collected from the discussion forums and course reviews.
On August 15 2011, Stanford professor Andrew Ng uploaded an intro video to YouTube for his free online Machine Learning course. On that same day, The New York Times featured his course (along with two other Stanford courses).
Andrew left Baidu earlier this year to work on his own AI projects. In a post on Medium, he announced that he is working on three different AI projects with Deeplearning.ai being the first one. He describes it as “a project dedicated to disseminating AI knowledge.”
8 million learners have signed up for his Machine Learning course. Andrew Ng is no longer at Coursera full time, but acts as the co-chairman of the board. He left Coursera in May 2014 to join Baidu.
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
At last I've successfully completed the specialization and earned my certificate!
Thanks to Mahmoud Badry for his Notes that help me in understandating the course more.