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How I Would Learn Data Science (If I Had to Start Over)

In this video, I talk about how I would learn data science if I had to start again from scratch. Learning data science and machine learning is a little bit different for everyone, but hopefully this video will help you to build the necessary foundation to tackle these fields.

1) Learn programming in python or R
– Advice for college students:
2) Learn basic statistics for data science
– Free courses:
3) Start projects as soon as you can
– Use to review code and work on basic projects
– Beginner projects video:
4) Take notes on what algorithms and packages others use
5) Learn the source code for the algorithms and try to implement them from scratch
6) Work on more advanced projects where you collect your own data or use advanced concepts like deep learning, nlp, or computer vision.
– Data science project playlist:

Additional tips:
1) Make studying data science a habit
– Ultralearning video:
2) Hold yourself accountable

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Why You Should NOT Learn Machine Learning!

Everyone tells you why you should be learning machine learning. It is the next ‘big thing’ after all. But in this video I’m going to be telling you why you should NOT learn machine learning.

There are many reasons for not starting machine learning. Some of these are:
1. Don’t get into ML just because it is popular. Basically do not FOMO into it.
2. Machine learning is a huge field, there are so many things to do. You might start it but eventually give up because you don’t know what exactly you want to build using ML/AI.
3. Just because you saw people getting paid more for machine learning jobs doesn’t mean you should start it. As you can still polish your skills in other computer science related fields.

Even though I mention why you should not be get into machine learning, this doesn’t mean that you should not at least give it a go. This video is only made for some people who want to get into ML/AI without having any major goal.

Let me know what you think about this in the comment section below.

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