AI is the new Electricity _ Andrew Ng
I was first introduced to the concepts of artificial intelligence in 2017 through Udacity and it was overwhelming. I had no prior knowledge of AI-related concepts, so the terms felt like they were coming from outer space.
For two years, all I knew were just pieces of the puzzle. I had no clue how the pieces fitting together to form the complete picture of Artificial Intelligence.
Luckily, after completing the first semester of my graduate studies in computer science, and also enrolling in Andrew Ng’s machine learning course on Coursera, everything just seemed to make a lot of sense.
If you are wondering whether to spend your precious time going through this course and how challenging it could be, then hopefully by the time you are done reading this article, you will know exactly what to do.
But first, let me throw some light on how this course is organized…
General Outline and Expectations.
The course is spaced out into 11 weeks with almost every week ending with a quiz and project. The quizzes and projects for each week are related to the lectures for that week which are offered by Andrew himself.
Just to give you an idea of the impact the course has had over the past years, here is a screenshot of the total number of enrolled students for the course from the website.
At the time of writing this article, the course has exactly 3,217,612 enrolled students with an average rating of 4.9 / 5.
Impressive right?
Looking at these statistics one can only imagine what exactly might be the cause of this. Me too!!. Part of what made me enroll was pure curiosity and the other part was the sheer passion to learn everything related to machine learning from the very foundation to the most recent advancements.
The programming language used throughout the entire course is Octave/Matlab programming language, which for the record you do not need to have any pre-knowledge of in order to successfully complete the course. So relax and breathe in.
How Difficult is the Course?
If you have not taken the course before you may be wondering how challenging the course might be. How many hours per week are required?
Personally, I found the course relatively easy to follow and understand the concepts. This is because I have had some prior exposure to the terms and concepts of machine learning.
Also, one of the main things that permitted me to successfully complete the course in a month with a deep understanding of everything is due to the fact that, during the first semester of my master’s program, I enrolled in courses such as Probability and Stochastic processes, Discrete Mathematics and others. This gave an added advantage in grasping the concepts relatively faster.
So if you have no prior knowledge of machine learning and this is your first course, things might feel a little overwhelming and you will need a little bit more time to let your brain connect the dots and makes sense out of what is going on.
The good news is that Andrew explains complex ideas in such a lucid and easily understandable format that anyone who is motivated to learn about machine learning can easily digest the information.
As an appetizer, some of the concepts that are covered in the course include the following; Regression, Classification, Neural Networks, Gradient Descent, Anomaly Detection, Recommender Systems, just to name a few.
Andrew also shared a lot of valuable implementation tips when building a machine learning model from his many years of building and shipping these models out.
After Completing the Course.
At the end of the course, you will have a deep understanding of the fundamental concepts that make up machine learning.
Personally, upon completion, I decided to combine what I learned from the course together with the knowledge I acquired from my graduate courses into a single article titled An end-to-end comprehensive summary of machine learning on my blog.
One amazing bonus you get from finishing this course is that you get to learn a new programming language, Octave/Matlab if you never knew it before. You can add this to your resume. Let’s just say it’s the icing on the cake.
Finally, if you make it to the very end of the course, you are awarded a beautiful certificate signed by Andrew Ng himself that you can showcase on your LinkedIn profile and probably increase your chances of landing that dream job or getting that raise you know you now deserve.
Conclusion — What’s Next…
Building a rock-solid foundation in any domain gives an 80% guarantee of succeeding in that field. If you are new to machine learning, I recommend you enroll in this course.
After you are done with the machine learning course, do not stop there. I urge you to continue with Andrew’s Deep learning specialization course.
There are 5 different courses in this specialization and the language of programming this time is Python using Jupyter notebooks.
At the moment, I just finished the first two courses of the specialization title Neural Networks and Deep Learning and Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization and currently enrolled in the third course.
So, for now, my comments regarding the deep learning specialization course are limited but I will write another article as soon as I am done with the specialization.
Thanks for Reading and Never stop Learning!
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