To become a proficient Machine Learning (ML) engineer, you need a strong foundation in key mathematical concepts. Focus on: Linear Algebra: Vectors, matrices, eigenvalues, and SVD. Calculus: Differentiation, partial derivatives, and integration. Probability and Statistics: Distributions, Bayesian probability, hypothesis testing, and descriptive statistics. Optimization: Gradient descent, convex optimization, and stochastic gradient descent. Discrete Mathematics: Combinatorics and graph theory. Information Theory: Entropy, mutual information, and KL divergence. These areas are critical for understanding and developing ML algorithms effectively.
So where’s the math? Linear algebra, calculus or even statistics?
Great , good wishes for you❤
What are the mathematical concepts one should learn to become a ML engineer...
To become a proficient Machine Learning (ML) engineer, you need a strong foundation in key mathematical concepts. Focus on:
Linear Algebra: Vectors, matrices, eigenvalues, and SVD.
Calculus: Differentiation, partial derivatives, and integration.
Probability and Statistics: Distributions, Bayesian probability, hypothesis testing, and descriptive statistics.
Optimization: Gradient descent, convex optimization, and stochastic gradient descent.
Discrete Mathematics: Combinatorics and graph theory.
Information Theory: Entropy, mutual information, and KL divergence.
These areas are critical for understanding and developing ML algorithms effectively.
@@SimplilearnOfficial what if when mathmatics nit strong like me but i am AI and ML student
Thanks for sharing this valuable information. Very helpful 👍
Glad it was helpful!
Thanks for this valuable information
Glad it was helpful!
Do we need to learn deep learning to become a ml engineer?
i love his voice
Ok can you plz also tell us about the difference between a MLE and a Data Scientist?
ML is part of AI & data science has parts of ai, ml, deep learning
Yo whats Lpa
Dollars.
Great