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When learning machine learning, focus on these algorithms first:
🔸 Linear regression
🔸 Logistic regression
🔸 Decision trees
🔸 Random forest
🔸 Gradient boosting
🔸 Neural networks (also CNN)
In this order
Knowing them will cover 95% of applied ML cases
The most useful part of math for applied machine learning is Linear Algebra
🔸 Vector multiplication
🔸 Vector norm
🔸 Matrix multiplication
🔸 Matrix inverse
Focus on learning that and you'll know enough for 95% of practical cases
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