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**BaDshaH** · 05-30-2023, 07:12 PM

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Last updated 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 115 Lessons (7h 20m) | Size: 859 MB

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you'll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Note: Third course will be started 30/05. I will update when It ready

WHAT YOU WILL LEARN
A deep understanding of the math that makes machine learning algorithms work.
Statistical techniques that empower you to get more out of your data analysis.
Fundamental skills that employers desire, helping you ace machine learning interview questions and land your dream job.
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
SKILLS YOU WILL GAIN
Bayesian Statistics
Machine Learning
Mathematics
Probability
Linear Regression
Linear Equation
Eigenvalues And Eigenvectors
Linear Algebra
Determinants
Calculus
Mathematical Optimization
Gradient Descent

About this Specialization
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Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you'll master the fundamental mathematics toolkit of machine learning.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works.
This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they're most applicable to machine learning and data science.

Applied Learning Project
By the end of this Specialization, you will be ready to
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformations
Apply concepts of eigenvalues and eigenvectors to machine learning problems
Optimize different types of functions commonly used in machine learning
Perform gradient descent in neural networks with different activation and cost functions
Describe and quantify the uncertainty inherent in predictions made by machine learning models
Understand the properties of commonly used probability distributions in machine learning and data science
Apply common statistical methods like MLE and MAP
Assess the performance of machine learning models using interval estimates and margin of errors
Apply concepts of statistical hypothesis testing

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