If you've chosen to seriously study machine learning, then congratulations! You have a fun and rewarding journey ahead of you.
Here are 10 tipsthatevery beginner should know:
1. Set concrete goals or deadlines.
Machine learning is a richfield that's expanding every year. It can be easy to go down rabbit holes. Set concrete goals for yourself and keep moving.
2. Walk before you run.
You might betempted to jump into some of the newest, cutting edge sub-fields in machine learning such as deep learning or NLP. Try to stay focused on the core concepts at the start. These advanced topics will be much easier to understand once you've mastered the core skills.
3. Alternate between practice and theory.
Practice and theory go hand-in-hand. You won't be able to master theory without applying it, yetyou won't know what to do without the theory.
4. Write a few algorithms from scratch.
Once you've had some practice applying algorithms from existing packages, you'll want to write a few from scratch. This will take your understanding to the next level and allow you to customize them in the future.
5. Seek different perspectives.
The way a statistician explains an algorithm will be different from the way a computer scientist explains it. Seek different explanations of the same topic.
6. Tie each algorithm to value.
For each tool or algorithm you learn, try to think of ways it could be applied in business or technology. This is essential for learning how to "think" like a data scientist.
7. Don't believe the hype.
Machine learning isnot what the movies portray as artificial intelligence. It's a powerful tool, but you should approach problems with rationality and an open mind. MLshould just be one tool in your arsenal!
8. Ignore the show-offs.
Sometimes you'll see peopleonline debating with lots of mathand jargon. If you don't understand it, don't be discouraged. What matters is: Can you use ML to add value in some way? And the answer is yes, you absolutelycan.
9. Think "inputs/outputs" and ask "why."
At times, you might find yourself lostin the weeds. When in doubt, take a step back and think about how data inputs and outputs piece together. Ask "why" ateach part of the process.
10. Find fun projects that interest you!
Rome wasn't built in a day, and neither will your machine learning skills be. Pick topics that interest you, take your time, and have fun along the way.
See the rest here:
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