How To “Ultralearn” Data Science: summary, for those in a hurry

By Benthecoder, Developer, Writer, Machine Learning and AI enthusiast.

See alsoHow To “Ultralearn” Data Science: optimization learning, Part 3How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4 Ultralearning data science begins with utilizing metalearning strategies.

The first step is to create a map that suits your life.

Don’t go ahead and plan out an impossible pathway that requires you to learn for 12 hours a day every week.

That’s just unrealistic, and you’ll experience burn out.

Be more pragmatic when you plan your journey: Take into account your personal responsibilities, hobbies, friends, and family, etc.

Another key thing is to find your idiosyncratic way of learning.

Everyone has their own learning methodology.

Don’t try to be someone else and copy their way of learning.

If you learn better through visualizations and videos, take MOOCs and go on YouTube.

If you prefer the old-fashioned textbook-learning style, go ahead and do so.

Nothing is stopping you from learning at your own pace.

 Distractions are everywhere.

Without focus, its almost impossible to learn.

Having the discipline to control yourself and the self-awareness that distractions come from within, not externally, is the first step.

Everything depends on you and you alone.

Grab a timer and set a time limit for your work.

The sense of urgency and the depletion of time provides the impetus to start your work instead of putting it off for an episode of “Friends.

” Next, sustain your focus, and get into the zone, as the programmers call it by setting up the right environment for yourself.

Then, use timeboxing to make sure your productivity is at the maximum level.

Finally, discover your mental power peaks to ensure you’re delegating creative tasks to moments when you’re highly energized and monotonous tasks for less active periods.

Our brain has its limits.

Don’t go pushing yourself too hard.

When you just can’t get anything done, go take a walk, play the piano, talk to your friends (in person).

Then try to focus again.

 With focus, learning becomes a bit easier.

Now comes the hard part.

Learning just by reading and memorizing is pointless.

That’s the college methodology.

It’s time to leave that behind and start learning effectively.

A few key takeaways from this part is to practice application.

Doing is substantially better than absorbing passively.

We’ve all learned things by doing, ever since we were infants.

We study the world and formulate our very own framework.

Want to learn machine learning?.Don’t start studying mathematics and stuff heaps of concepts and facts into your brain.

Install an IDE (PyCharm/VS Code), search up YouTube videos/websites with tutorials on how to implement machine learning, and choose a language (preferably Python).

That’s it — that’s all you need to get the essence of machine learning and the flow of it.

Nonetheless, great data scientists have a knowledge of statistics, Python, business acumen, and machine learning all packed in their heads — and they’re prepared for whatever kinds of data people throw at them.

With optimization learning, you’ll be prepared and ready to face any kind of hurdles that come your way.

 Cultivating deep understandingThree steps for experimentationA deep understanding of a concept allows you to put pieces together and to use this understanding to solve problems and formulate new ideas.

By knowing the essence of something, novel solutions to complex problems can be engendered.

To achieve deep understanding, start by getting the basics right, and understand it inside out.

Only with this fundamental knowledge of the groundwork can a person start branching out into more in-depth concepts in the semantic tree.

Another vital thing is to borrow from experts.

Experts have a copious amount of experience in the field and are adept in their workflow.

By replicating how an expert solves a problem from top to bottom, you’ll eventually acquire the skills and expertise through repeated replication.

Experimenting with data and pipelines is the underlying ingredient of data science.

Models can be biased and filled with errors — only with perpetual experimentation with different features (feature engineering) and with algorithms can one improve a model.

By putting constraints on which features to choose and hybridizing your ML tools, you can create a good model.

Be bold and courageous!.Think big and go crazy.

Experiment intensively.


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