Description for Mathematics in ML Specialization
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
Pricing for Mathematics in ML Specialization
Use Cases for Mathematics in ML Specialization
FAQs for Mathematics in ML Specialization
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
Alternative Tools for Mathematics in ML Specialization
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.
Learn to implement and apply unsupervised learning techniques, focusing on clustering and dimension reduction algorithms, in a business environment.
Learn linear algebra concepts, including eigenvalues and eigenvectors, and apply them to practical problems using Python and Jupyter notebooks.
Apply mathematical concepts to real-world data, derive PCA from a projection perspective, comprehend orthogonal projections, and master Principal Component Analysis.
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.
Featured Tools
The course highlights the curriculum focused on statistics and machine learning, covering descriptive statistics, data clustering, predictive model development, and analysis capability development.
Generative AI for Your Benefit. Utilize Generative AI to develop and instruct personalized assistants.
The course outlines the learning objectives for understanding XGBoost algorithm theory, performing exploratory data analysis, and implementing XGBoost classifier models using Scikit-Learn.
Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.
Gain knowledge in MLOps principles, machine learning frameworks, and sophisticated Kubeflow tools to optimize production system workflows.