Mathematics For Car Learning Too Data Science Specialization

A deep agreement of the math that makes automobile learning algorithms run.

Statistical techniques that empower you to become more out of your data analysis.

Mathematics for Machine Learning in addition to Data Science is a foundational online program created past DeepLearning.AI together with taught past Luis Serrano. This beginner-friendly Specialization is where you lot’ll principal the primal mathematics toolkit of motorcar learning.

Many automobile learning engineers too data scientists need assistance alongside mathematics, too even experienced practitioners tin experience held dorsum by a lack of math skills. This Specialization uses innovative instruction inwards mathematics to help y'all larn chop-chop as well as intuitively, amongst courses that use easy-to-follow plugins too visualizations to assistance you run across how the math behind automobile learning really plant. 

This is a beginner-friendly program, amongst a recommended background of at to the lowest degree high schoolhouse mathematics. We too recommend a basic familiarity alongside Python, as labs function Python to exhibit learning objectives inwards the environment where they’re almost applicable to motorcar learning together with data science.

Represent data every bit vectors as well as matrices in addition to identify their properties using concepts of singularity, place, too linear independence

Apply mutual vector and matrix algebra operations like dot product, inverse, and determinants 

Express sure types of matrix operations as linear transformations 

Apply concepts of eigenvalues in addition to eigenvectors to auto learning problems

Optimize different types of functions normally used inwards auto learning

Perform slope descent in neural networks alongside unlike activation too price functions 

Describe too quantify the dubiety inherent in predictions made by automobile learning models

Understand the properties of ordinarily used probability distributions inward auto learning as well as data scientific discipline

Apply common statistical methods like MLE together with MAP

Assess the operation of automobile learning models using interval estimates in addition to margin of errors 

Apply concepts of statistical hypothesis testing