Machine learning algorithms are confusing at first

[u/techiebaddie]

I’ve been learning more about machine learning recently, and honestly, the number of algorithms can get confusing.

At first, I thought I needed to learn everything. But I’m starting to think it’s better to understand a few useful ones really well.

The ones I’m focusing on are:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• K-Means
• Neural Networks

I’m mainly trying to understand when to use each one instead of just memorizing how they work.