Here are some of my points about #MachineLearning:
- Machine Learning means learning from Data.
- Machine = Your machine/computer Learning = Finding patterns from data
- Machine Learning is just Data + Algorithms, but Data is more important.
- Feature extraction is key. If total prediction power is 100% then the effort of feature engineering = 80% and the effort of the learning algorithm = 20%.
- Overfitting is when your algorithm is memorizing instead of learning.
- If you have small amounts of data then you’re better off using more simple models (linear&logistic regression). If you have large amounts of data you can try out more complex models (Deep Learning, etc.)
- To avoid overfitting, always use regularization
- Training is the most important part of Machine Learning. Choose your features and hyper parameters carefully.
- Machines don’t take decisions, people do.
- Data cleaning is the most important part of Machine Learning. You know the saying: Garbage in Garbage out.
10a. Data cleaning is a large part of #DataScience. Don’t be surprised if you spend more time here than you do with your friends and loved ones.
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👉 What else would you add to the list?
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