Machine Learning Mathematics/Data Analysis/Probability Theory/Statistics/Linear Algebra/Mathematical Analysis

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Руслан Сенаторов

Published on Streamed live on Dec 14, 2024
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Turn your videos into live streams with https://restream.io 🚀 Join the community: https://boosty.to/SENATOROV 🍑 Subscribe to Telegram: https://t.me/RuslanSenatorov 🔥 Start working with cryptocurrency on Bybit: https://www.bybit.com/invite?ref=MAN2VD 💰 Donate: https://www.donationalerts.com/c/sena... 💰 Become a sponsor: (USDT TRC20) TPWP9kuqqetDNPeLjAe51F1i2jPxwYYBDu (USDT BEP20) 0xf3db7ce90a55d1d25b7a6d1ded811fb2a7523f3d Main mathematical topics: Linear algebra: Vectors and matrices Matrix operations (multiplication, transpose, trace) Eigenvalues ​​and eigenvectors Inverse matrices Decompositions (QR decomposition, SVD decomposition) Systems of linear equations Mathematical analysis: Limits and continuity Derivatives and partial derivatives Gradients and optimization Integrals Function optimization Mean value theorem Multivariate analysis Probability theory: Random variables Probability distributions (normal, binomial, Poisson) Conditional probability Bayes' theorem Laws of large numbers and central limit theorem Statistics: Mathematical expectation, variance Statistical hypotheses and tests Regression and correlation Parameter estimates (maximum likelihood, least squares) Data analysis and visualization Descriptive statistics Interval estimation Hypothesis testing Regression analysis Tags: Mathematics for Machine Learning, Linear Algebra in Data Science, Mathematical analysis for Machine Learning, Probability Theory and Statistics, Mathematics for Analysis data, Vectors and matrices, Derivatives and gradients, Probability distributions, Regression and correlation, Machine learning, Data Science training, Mathematical foundations of ML, Machine learning algorithms, linear algebra for machine learning, probability theory in data science, mathematical analysis in ML, statistics for data analysis, data science from scratch, machine learning for beginners, Python for data science, R for data analysis, numpy, pandas, scikit-learn, deep learning, neural networks, artificial intelligence, #mathematics #datascience #machinelearning mathematics from scratch, mathematics for data science, mathematics for machine learning, mathematics for dummies, mathematics for beginners, mathematics for programmers, mathematics for data science, math tutor, math teacher, math teacher, math mentor, math teacher, data science tutor from scratch, tutor in higher mathematics, tutor in mathematics for adults, mathematics for correspondence students, mathematics for data analytics

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