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Ultralearning is the first book of Scott H Young but he has been writing about ultralearning for years on his blog. I love his blog and follow it pretty regularly so when he first wrote about his book project, I was very excited and when the book was finally published, I knew I had to read it. Scott H Young is famous for his MIT challenge, a project for which he followed MIT classes and took the corresponding exams during one year. The goal was to come as close as possible to a standard MIT education, but in one year instead of four.
In this post, I will give you an overview of the main principles of ultralearning as described in the book. However, if you are interested in the subject, you should really read the book because there are so many inspiring examples.
What is ultralearning?
Ultralearning is a strategy for acquiring skills and knowledge that is both self-directed and intense. Ultralearner pursue extreme, self-directed projects and employ similar techniques to achieve them. The specifics may vary from one ultra learner to another though.
Why does ultralearning matter?
Ultralearning is not easy but, according to Scott H Young, it has several advantages:
- For your career, learning hard skills rapidly can have a much bigger impact than making long hours and working hard on the job.
- For you personally, you can master new activities and hobbies which will bring you deep satisfaction and self confidence.
- In the modern economy, it is not enough to get a good education and work hard every day. You need to push to the higher skilled category, where learning is constant, not to be pushed to the lower skilled category at the bottom
- Regarding formal education, universities are expensive (this is particularly true for North America, somewhat less for Europe) and it is not always convenient to go back to school when you have a job and a family.
- As technology offers us more possibilities to learn, ultralearning has become easier than ever.
What are the principles of ultralearning?
- Metalearning: start by learning how to learn your chosen subject. Do your research and come up with a strategy.
- Focus: Learn to concentrate and focus on learning.
- Directness: Learn by doing the things you want to become good at.
- Drill: Work specifically on your weakest points. Break down complex skills into small parts and learn to master them.
- Retrieval: Test yourself and push yourself to actively retain information either than passively review it.
- Feedback: Look for feedback and learn to use it correctly.
- Retention: U verstand what you forget and why. Learn to remember things longer.
- Intuition: develop your intuition through play and exploration of concepts and skills.
- Experimentation: explore new possibilities and go out of your comfort zone.
What I learned from the book
The book helped me to understand better why some of my learning projects, without being failures, did not work as well as I had hoped. For instance, in my Spanish learning project, I was focused and learned every day, which was good. I also worked on retrieval and tried some drills as I tried to work more specifically on conjugation for instance. However, my approach was not direct enough. I worked a lot on fun websites such as Duolingo instead of speaking directly to Spanish speaking people or reading whole books in Spanish. I also did not have real feedbacks.
For my Python project, the problems were similar. I reviewed the rules of the Python language, which was useful, but I did not work enough on real Python projects and I had no feedback either on the quality of my coding. If I do a coding project again one day, I will take a very different approach: concentrate on creating a video game, for instance.
How I want to apply this to my data science project
- Metalearning: I already did this part of the work. I have been interested in data science for some time now and I checked different resources. For instance, I checked lists of books but felt I needed something more direct. I tested a few data science courses on Coursera and decided to take the IBM data science course as it is complete, made for beginners, and based on Python which I already know. Indeed, some courses are based on R and I did not feel like learning both R and data science from scratch.
- Focus: This should not be a problem. I am good at focusing and Coursera courses are also made in a way that encourages you to work regularly. For instance, you have deadlines for the different tests and projects. You also win certificates for each course of the track, which is great for motivation.
- Directness: the courses include different projects and exercises so I will have to work directly on what I learned. When I am more advanced on the subject, I will have to evaluate if this is enough or if I need additional projects.
- Drills: I will probably have to add some Python practice to get very good at it.
Retrieval: the courses offer several tests, which is helpful for retrieval.
Feedback: as the final projects of the courses are reviewed by fellow students, I will have some feedback. There too, I will have to reevaluate the project to make sure this is enough.
Retention: I will have to reevaluate the project after I am more advanced to see how I am doing with retention. I am thinking of using an over learning strategy and take some more courses on data science/Python.
Intuition: this is too early to define this point now. I need more experience on the subject
Experimentation: this is too early to define this point now. I need more experience on the subject