How I completed Machine Learning course taught by Prof Andrew Ng on Coursera?

And yes you too can, just make efforts and results shall be there

Short story: Why I started to learn machine learning?

Because I like to make cool stuff and play around the knowledge that I can truly understand and communicate with all of you :)

It all started when I chose machine learning as an elective course at my university. From there I got a brief overview of supervised and unsupervised machine learning algorithms like linear and logistic regression, decision tree, K means clustering …etc

But there at the same time, hands-on experience was missing so to fill that gap I needed to find some other way. Most importantly, my degree of curiosity increases whenever I learn by doing at my own pace. Maybe that’s because I find myself :

“Somewhere in the intersection of Learn, Create and Share”

So I started searching over the internet about machine learning courses that can give me hands-on experience at a beginner level. It took some time, maybe approx a week but eventually, I discovered the Machine Learning course by Prof Andrew Ng with the help of an online community i.e Facebook ML groups, Review videos on Youtube.

“ The most well known and popular course in Machine Learning Community all over the globe ”

Luckily summer holidays were also on its way and Course duration was approx (54 hours). So I started preparing my mind to be consistent and spend these days wisely to learn machine learning.

To me, it took approximately 3 months to complete the Machine Learning course.

No need to Rush!

The following are some of my experiences that I would like to share with you. Not all but some may benefit you in some way. Maybe for completing Machine Learning or any other course.

So let’s get started

What’s so special about this course?

In my perspective that is the easiest question to answer because of the following reasons :

1. Firstly, this course is taught by Prof Andrew Ng (One of the pioneers in the field of Artificial Intelligence)

2. Secondly, you will learn by doing 😃 and making interesting projects like spam classification system, movie recommender system, …etc

3. Last but not least this was the first online course that I completed successfully and during that time I discovered a community where I can make my contributions like in Medium Platforms such as towards data science, Analytics Vidhya, …etc

Ask yourself: Why do you want to learn this course?

First of all, you need to contemplate on this question because of just two simple reasons:

1. Graph of doing passionate work is not linear

There comes a time when you don’t feel like doing work that you were passionate about maybe because you are not fully able to understand a particular algorithm, ..etc. But if you are truly curious and passionate to accomplish your goal then naturally you will get back to work again.

So it is very important to set goals before you get started to work on anything. Question is How to make goals ?. I will share a very simple model to make goals in the latter part of this blog.

2. Don’t fall into Marketing Trap

Secondly, we don’t want to fall into the marketing trap of data science, machine learning, artificial intelligence, ..etc. Currently, these terms are now a buzzword.

My point here is that you should know why you want to learn this course?

It should be your decision not others to make you decide to learn any cool stuff.

Take Little little baby Steps like Learning rate in Gradient Descent Algorithm 😆

This is the famous optimization algorithm you will learn in the 2nd week of this course.

This course takes approx 54 hours or 3 months to complete. Yes it may vary from person to person but based on my beginner level experience I’ll recommend that you should give at least this amount of time because :

• To build strong foundations of Machine Learning

• We aren’t just interested in learning but making cools stuff, collaborating with international classmates, and sharing knowledge to the community

Organize daily work tasks

My experience taught me that there’s a difference between doing work with and without setting a goal.

One of the habits I developed was that I used to write it down a couple of tasks before I got started to do my daily routine tasks. Even though on starting I wasn’t able to complete those tasks 😜

While turning over the pages I just found this :

Now the most important question is :

How to make Goals?

All credit goes to my principles of management teacher!

Storytime: It was way back in the 7th semester when I chose principles of management elective course. One of the best choices I made in choosing a course at university. There I learned that Goals should

S ▶️ Specific 👉 What do you want to do?

M ▶ Measurable 👉 How will you know when you’ve reached it?

A ▶ Achievable 👉 Is it in your power to accomplish it?

R ▶ Realistic 👉 Can you realistically achieve it?

T ▶ Timely 👉 When exactly do you want to accomplish it?

“If you can’t measure it, you can’t improve it” by Peter Drucker

The lesson to be considered after making goals 👇

“ Sometimes things don’t always work out the way we planned ”

Observe How these mountains are also teaching us that ups and downs are part of life.

Certifications don’t make you happy…

but sharing and collaborating with the online community does!

Storytime: Before getting started into machine learning stuff I remember that I got inspired after looking into certification of ML enthusiasts on different FB groups. So I told myself I also want that and worked for it for approx 3 months but this certification did not make me feel that I did something!

Instead, sharing and collaborating my understanding of Machine learning concepts with an online community increased my curiosity and see that’s why I am writing this blog, even though I am not a regular writer 😆

“The journey is what brings us happiness, not the destination” by Dan Millman

👉 Learn, Create, and Share

These 3 dimensions separated may or may not make a difference but together it creates a path that leads towards doing something meaningful.

My experience taught me that if you are passionate about something like making projects using machine learning, …etc then create something out of it and share it with online platforms. Believe me, like-minded people will reach out and magical things happen. Because you never know who might end up reading, making, sharing your projects all over the globe.

Just to give you an example below are some of the messages I received after collaborating with like-minded people 👇

The most important lesson is that the work you share does not have to be any big project stuff. It’s simply like when you are doing something meaningful then naturally, the voice inside you says that 👉 NOW Share it!

For example, in Week 7 of the Machine Learning Course, you learn about Support Vector Machines and you need to submit an assignment by using the libsvm library. So after figuring out I wrote a tutorial on How to use libsvm in Octave ? and posted it in a discussion forum of Coursera. Hence many students enrolled in that course are now getting benefits out of it 👇

Libsvm: It is the most popular open-source machine learning library developed at National Taiwan University written in C++

or you could play around with a particular concept in machine learning like the learning rate parameter in Gradient descent algorithm, …etc 👇

Discuss with your international classmates and Mentors

One of the good things about Coursera is its discussion forum if you like to ask questions ❓ There you can make study groups or ask about any problem you are facing in the course or share any valuable information to your classmates.

Most Active Mentors: Tom Mosher and Neil Ostrove

Out of these two guys, one of them will surely respond within 24 hours or less than that time. Mentors like them helped many of us throughout this course.

Curiosity at its peak

I just remembered that when I was in week 5 of the course. I posted a thread of about simplifying regularization term in a Cost function of Neural Network 👇

The interesting part is that It was raining outside ☔️and I don’t know why I was doing this simplification.

Don’t forget to Workout 💪

Exercise is really important because it has a direct impact on mental health. It relieves stress, improves memory, and helps you sleep better. I used to go jogging 🏃early in the morning, then I make myself breakfast and ultimately start learning ML ☺

That was the time when I was consistent in terms of doing a workout. I hope to be now as well 😃

Check out my tutorials I wrote recently

Last month I attended a virtual workshop on Streamlit in my local community. It was a very interesting and valuable session. So I thought let’s make a tutorial out of it and share it with the online community.

Both of the below tutorials got published at towards data science (A treasure for data science and machine learning enthusiasts)

You will also learn about Vectorization in Machine learning Course. A skill by which you can make your code execute fast 🚀

Last but not least!

“Don’t worry about it if you don’t understand ” by Prof Andrew Ng

One of the Pioneers in the field of Artificial Intelligence

It’s completely okay if you are facing any trouble understanding machine learning concepts. The only thing that matters is your passion and eventually, you would be able to grasp those topics at the right time.

I also still don’t understand how the backpropagation algorithm learns parameters? even though I haven’t made an effort to understand that 😆

“Even if you are best of best there’s always a chance of failure” by Elon musk

Conclusion

Now it’s time to build useful stuff. I hope you enjoyed reading my journey of learning ML.

I am looking forward to knowing What you made using machine learning?

Thank you very much for your precious time.

Contact

Feel free to comment below or ask it via :

Gmail: [email protected]

Twitter: https://twitter.com/JalalMansoori19

Github: https://github.com/jalalmansoori19

Medium: https://medium.com/@jalalmansoori

If you liked what you read, leave me a tip, or even share my work on social media! Any support is appreciated. 😊