How was your journey to the world of machine learning? I believe it hasn’t been easy at all. Luckily, you are not the only one. Many people confessed that the begin steps were the hardest part when they study this field, as there are too many new concepts they must master before being able to craft something themselves, and therefore, many beginners have given up in this stage. Then how did the rest surpassed this phase? This post will give you some advices to overcome beginning phase when learning machine learning.
Master Fundamentals:
Before diving into complex topics, make sure you have a solid understanding of linear algebra, calculus, statistics, and probability. These are the building blocks of machine learning.
To build a tall building, you need strong base, and so is learning machine learning. Basic tools like math and statistics are the foundation that explain to you how algorithms work. Before get into any complex topics, mastering fundamentals to understand machine learning concepts, and by that, you can create, improve, and fix models easily. So, taking the time to learn these basics is like building a solid foundation for your machine learning journey.
Practice makes perfect:
Think about learning how to play a game or ride a bike. You get better the more you practice, right? Likewise in respect of machine learning. The “10,000 hours rule” states that it takes roughly 10,000 hours of practice to become exceptionally effective at something. Imagine it as a video game where you can learn new strategies as you play. Practice in machine learning entails trying out various issues and making errors. These mistakes teach you what works and what doesn’t. It’s like falling off a bike while learning to balance. The more you practice, the more you figure out how to make models that solve problems. So, if you want to be a machine learning whiz, roll up your sleeves and practice – it’s the key to becoming really skilled! You can start by form a daily habit of working on the subject. This helps you keeping track of you learning journey.
Overcoming paralysis by choice:
It’s acceptable to make errors when you first start. It can take a lot of failures before you succeed in machine learning, which makes it difficult to get started. The most essential thing is to grow and improve through learning from mistakes.
Instead of concentrating on others’ development along your journey, concentrate on what you have achieved and the work you have done. Because everyone learns differently, you might begin at the same pace yet progress in various ways as time goes on. Compare your development to your previous self, not to others. You can learn at your own speed and achieve the best outcomes by concentrating on yourself.
Learn from experts
Numerous methods exist for learning data science. You can attend meetups, read articles, watch videos, sign up for online courses, etc. However, experience is one thing that you cannot “learn.” something you must acquire via years of experience in the industry. There is a lot to learn from data science experts, including their expertise in leading end-to-end machine learning and deep learning projects, to their philosophy for building a data science team from the ground up, etc. When you learn from them, you skip the confusion and mistakes. So, if you’re new to machine learning, learning from experts is like having a shortcut to getting really good.
Here are a few data science experts to learn from: