In the previous post, I talked about my goal for my YouTube channel which was to gain mastery at data science.
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               See slide 1

And now, in this post, I want to talk about the first big project that I want to undertake in the context of pursuing that goal.

​Namely, the project is to finish 5 Kaggle competitions in the top 10%. To be more specific, I want to finish 5 “completed” Kaggle competitions in the top 10%.
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The reason why I want to tackle only “completed” competitions (instead of “active” ones) is because, this way, I can look up what other people have done to get good results and then learn from that. By taking this approach, I should be able to actually get into the top 10% of the competitions. Another, more practical, reason for doing only “completed” competitions, is that this way, I don’t have to wait until the competition is finished to see how I have actually ranked.

Okay, so that is what the project is generally about. However, on top of that, I want to introduce two more restrictions in order to narrow down the scope of this project.

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Namely, the first one is that I am only going to consider competitions with tabular data. So, at the moment, I am not interested in competitions about computer vision or natural language processing. That is something that I want to do more in depth later on.

And the second restriction is that I only want to use “traditional” machine learning algorithms like logistic regression or random forests. So, I am not interested in using any deep learning algorithms. That is also something that I want to do more in depth later on.

Okay, so that is the scope of the project. And now, let me list out all the things that, I think, I will need in order to complete this project.

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​The first thing, that I will need, is some knowledge about Kaggle itself.
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And that’s just so that I understand how the website is generally structured. And that’s because, this way, I am best prepared to make the most of all the available resource and therefore make sure I learn as much as possible from the competitions.

Next up, I will need some knowledge about machine learning.

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And here, in particular, I need to know how the machine learning process works. So, how to prepare the data and train and evaluate the machine learning models. And then, I also need to know how the machine learning algorithms themselves work.

The reason why I separated the “process” from the “algorithms” is because, technically, you only need to know how the “process” works in order to finish a Kaggle competition. You could, for example, just use a grid search to find the best values for the hyperparameters of an algorithm. However, you are obviously better prepared to train the algorithms, if you know how they actually work. So that’s why, I made this separation.

Okay, so then, lastly, I will also need some knowledge about the actual tools to implement the project. And here, first and foremost, I need to know Python.

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This is, probably, the most used programming language in machine learning. So, it makes sense to have a solid understanding of that. And then, with this as the foundation, I will also need some knowledge about some specific Python libraries.

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So, in order to load and manipulate the data, I will need to know Pandas. Then, in order to visualize the data and do exploratory data analysis, I will need to know a plotting library. And here, I am actually not sure yet which library I should learn. That’s why I wrote down both Matplotlib and Plotly. And lastly, in order to do the actual machine learning, I will need to know Scikit-learn.

So, those are all the things that, I think, I will need to know in order to complete the project. And for all those topics I will create tutorials.

And here, you might think that it is not really necessary to study all those things in depth, if I just want to “finish” 5 Kaggle competitions. And you might be right if I actually just wanted to “finish” them. In that case, I could, for example, simply look at the Kaggle forums to see what other people have done and then just copy the things that I need to get into the top 10%.

However, at the end of the project, my goal is to be able to basically pick a competition at random and then be able to produce very good results without looking anything up in the Kaggle forums. And therefor, I will need a solid understanding of all the listed things.

So now, let me write out the order in which I actually intend to learn them so that you know what tutorials you can expect next.

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So first, I will cover Python, Pandas and a plotting library. Then, I will get to the machine learning part. And here, I have also listed all the algorithms that I intend to cover. And, as you can see, I have actually already covered three of those, namely Decision Trees, Random Forests and Naive Bayes. After that, I cover Scikit-learn and Kaggle. And then, finally, I will do the 5 Kaggle competitions. And here, I have also listed the first two Kaggle competitions that I want to do, namely the Titanic competition and the House Prices competition. Those are both competitions where a lot of people have already participated. So, I think they are a good starting point for this project.

Okay, so this is the road map for completing the project. So, we are basically at the end of this post. But before I actually end it, let me first refer back to the previous post.

Namely, as I mentioned earlier, in that post I stated that my goal is to gain mastery at data science.
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               See slide 10

And the way I decided to measure that “mastery” is by using the number of subscribers that I have on my YouTube channel.

So now, I would like to make a prediction about how the number of subscribers will grow during and after the project. And my prediction is that I will be at the stage of 20,000 – 40,000 subscribers after I have uploaded the last video for this project (my hope is that I will be even one stage above that at 100,000 subscribers).

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And the reason I think that this will happen is because, by completing the project, I will have basically created a single resource where somebody can go from “knowing nothing about Python” to “getting good results in a Kaggle competition”. And, I think, that this would represent a very valuable resource for somebody who wants to learn about machine learning.

So, we will see how good that prediction actually is, once I am done. And now, there is only one thing left to say, namely: Let’s get to work.

​In this post, I want to talk about what my goal for my YouTube channel is and, more importantly, why I want to pursue it.

The goal is pretty straightforward, and it can be easily stated, namely: I want to reach 1,000,000 subscribers on my YouTube channel.
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Okay, so that’s the goal. And now, let’s get to the more important part: Why do I actually want to pursue this goal?

​And the answer to that question is also pretty straightforward, namely: I want to reach 1 million subscribers because I want to be happy.
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So, why do I think that having 1 million subscribers would make me happy?

Well, there are certainly many things that can influence one’s happiness.

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And it’s probably difficult to say what all those things exactly are and how much influence they have. However, one thing that we can be fairly sure about, that it is important, is your work.

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And that’s simply because for the fact alone that so much time of your life is spent at work. So, it stands to reason that it, overall, has a big impact on your happiness level.

Okay, so if that’s the case, then the logical next question to ask is: What makes work fulfilling and satisfying so that it ultimately leads to happiness?

And based on the books Drive by Daniel H. Pink and So good they can’t ignore you by Cal Newport, the answer to that question seems to be something called self-determination theory. It states that there are three components of work that make it worthwhile and fulfilling.

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• Mastery: being good at what you do
• Autonomy: having control over what you do and how you do it
• Purpose: having a positive impact on other people with what you do

So, those are the three components of fulfilling work. And out of those three things, you have the most direct control over mastery. You can’t just demand, for example, to be put in charge of an important project at your work (i.e. get more autonomy), if you don’t have the necessary skills. Likewise, you can’t just demand, for instance, to be part of a company that is doing something worthwhile (i.e. get more purpose), if you don’t have the necessary skills.

But what you can do, is to simply pick something that interests you (in my case: data science) and then put in time and effort to become good at it.

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If you do that, then you will already get one component of fulfilling work, namely mastery.

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On top of that, you can leverage it to get the other two components as well. For example, if you are really good at something, then you are actually more likely to be put in charge of an important project. Or if you are really good at something, then you can more easily switch to a company that is doing something worthwhile.

So, after all, my goal is not really to reach an arbitrary number of followers on a social media platform. My true goal is to gain mastery at data science.

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And since teaching is one of the best ways to learn (and thereby gain mastery), I decided to start a YouTube channel where I try to explain my understanding of data science concepts. And now, I will simply use the number of subscribers as a proxy for my level of mastery. Because if many people decide to follow me and my content, then that must mean that I am, at least, somewhat good at what I do.

And then, having at one point (hopefully) reached 1 million subscribers, I should definitely be able to make a living with that. And if that’s the case, then (in contrast to a regular job) I would have complete autonomy over what I do and how I do it. So, what I choose to spend my time on in terms of what topics I want to learn or what projects I want to do. And also, how I spend my time. Meaning, I could structure my day however I want. So that’s how I could leverage my mastery to gain more autonomy in my life.

And with regards to the third component of fulfilling work, namely purpose, having reached 1 million subscribers would mean that I have helped a lot of people to learn more about data science. That, in and of itself, is already a great purpose. But on top of that, having gained some mastery in that field, I am then also more capable of actually contributing to a purpose or cause that might become important to me in the future. Because, at the moment, I don’t know yet what that specific purpose could be. So that’s how I could also leverage my mastery to gain more purpose in my life.

So, in conclusion, reaching 1 million subscribers would mean that I gained mastery at data science, which I could then leverage to get more autonomy and purpose in my life. And this would mean that I would have fulfilling and worthwhile work which is an important contributor to one’s happiness. So, that’s why I want to reach 1 million subscribers.

But, what makes me actually think that I am able to reach that goal?

Well, the most important thing for gaining subscribers is to provide value. And in the context of educational videos, “providing value” means, in my opinion, providing helpful and clear explanations.
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And, as of November 23rd 2020, I have created 50 videos, so far. With that, I have reached a little bit over 1,700 subscribers and I got almost 150,000 views in total. So, apparently, I have provided, at least, some kind of value. Therefore, if I just keep producing videos (eventually hundreds of them), then, I think, it is basically inevitable that the channel keeps growing.

My rational goes like this:

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If I am able to get one thousand subscribers, then I can also get a couple of thousands of subscribers. And if I can get that many subscribers, then I can also get ten thousand subscribers. And if I can get ten thousand subscribers, then I can also get a couple of tens of thousands of subscribers and so on. So, that’s why I think I am able to reach 1 million subscribers.

But, the really cool thing about this is that, even if I ultimately don’t reach that goal, at a very minimum, I will gain mastery at data science in the process of trying to achieve that goal. And together with the projects that I have built along the way and the following that I amassed along the way, I can leverage that mastery to get a regular job in the field of data science. And that job will then probably also provide a good amount of autonomy and purpose.

So, there is nothing really to lose here. There is only something to gain. And that’s also a reason why I want to pursue this goal.

And with that, we have reached the end of this post. And in the next post, I will talk about the first big project that I want to undertake in the context of pursuing that goal.