Home AI Courses Mathematics in ML Specialization

Mathematics in ML Specialization

(0 reviews)

Machine learning mathematics. Find out about the mathematical prerequisites for applications in machine learning and data science.

Key AI Functions:Eigenvalues And Eigenvectors,Principal Component Analysis (PCA),Multivariable Calculus,Linear Algebra

Description

Reviews

Alternative Tools

Description for Mathematics in ML Specialization

This specialization bridges the gap between fundamental mathematics and its application in Machine Learning and Data Science, fostering an intuitive understanding and connection to these fields.

The initial course covers linear algebra, including the definitions and manipulation of vectors and matrices, and their relationship to data.

The second course on multivariate calculus focuses on optimizing data fitting functions, building on the linear algebra concepts from the first course.

The third course on dimensionality reduction with Principal Component Analysis uses the mathematics from the first two courses to compress high-dimensional data, requiring proficiency in Python and numpy, and includes practical mini-projects using Python on interactive notebooks.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Imperial College London

Duration: 1 month at 10 hours a week

Schedule: Flexible

Reviews for Mathematics in ML Specialization

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Microsoft 365 Copilot: Personal Productivity for All

ML in Production

Modern Robotics: Mechanics, Planning, and Control Specialization

Interpretable ML Applications: Part 2

Fraud Detection on Financial Transactions with ML on Google Cloud

Alternative Tools for Mathematics in ML Specialization

Linear Algebra in ML & Data Science

Apply linear algebra concepts like linear independence, rank, singularity, eigenvalues, and eigenvectors to analyze data and solve machine learning problems using standard vector and matrix operations.

#Eigenvalues And Eigenvectors #Linear Equation

Unsupervised Machine Learning

Learn to implement and apply unsupervised learning techniques, focusing on clustering and dimension reduction algorithms, in a business environment.

#Cluster Analysis #Dimensionality Reduction

ML in Mathematics: Linear Algebra

Learn linear algebra concepts, including eigenvalues and eigenvectors, and apply them to practical problems using Python and Jupyter notebooks.

#Eigenvalues And Eigenvectors #Linear Algebra

ML in Mathematics: PCA

Apply mathematical concepts to real-world data, derive PCA from a projection perspective, comprehend orthogonal projections, and master Principal Component Analysis.

#Dimensionality Reduction #Python Programming

Data in Machine Learning

In brief, this course instructs students on the effective management of data biases, the prevention of overfitting, and the enhancement of model accuracy through the implementation of appropriate testing methods and feature engineering.

#computer programming #python programming