Skip to main content
Category

Online training

STATISTICAL LEARNING

By OPPORTUNITIES: Training, Course, Free training, Format of the training, Online training, English, Training fee, Other, Up to three months, Duration of training, Type of training, Language of the training, Country providing the training, OPPORTUNITIES
STATISTICAL LEARNING

14.09.2023 |

This introductory-level course in supervised learning covers regression and classification techniques, including linear and polynomial regression, logistic regression, cross-validation, and more. It also touches on some unsupervised learning methods like principal components and clustering. The course emphasizes practical understanding without heavy math, using R for computing and providing detailed tutorials on its usage. Explore modern data analysis essentials with us.

Prerequisites: Foundational knowledge in statistics, linear algebra, and computing is recommended.

Instructors: Learn from Trevor Hastie, Professor of Statistics at Stanford University, and Robert Tibshirani, Professor in Health Research and Policy and Statistics at Stanford University.

Details

Target audience

Digital skills for ICT professionals

Digital technology

Digital skills

Level

Basic

Format of the training

Online

Training fee

Free training

Duration of the training

Type of training

Language of the training

English

Country providing the training

Other

Classification

Single opportunity

R PROGRAMMING FUNDAMENTALS

By Course, Free training, Format of the training, Online training, OPPORTUNITIES: Training, English, Training fee, Other, Up to three months, Duration of training, Type of training, Language of the training, Country providing the training, OPPORTUNITIES
R PROGRAMMING FUNDAMENTALS

13.09.2023 |

This course delves into the fundamentals of R, a popular programming language and software environment extensively utilized by data analysts, statisticians, and data scientists for statistical computing and graphics across the globe. It provides an introductory journey into R, encompassing installation procedures and foundational statistical operations. The course equips you with the skills to handle variables and external datasets, create functions, and offers insights from one of the R language’s co-creators, Robert Gentleman. Explore the world of R and enhance your statistical computing capabilities with this comprehensive course.

This course equips you with core competencies in R, covering data types, variables, vectors, matrices, lists, data frames, data import, logical statements, loops, functions, data plotting, visualization, and basic statistical functions. Prior experience in a scientific or engineering discipline is helpful but not mandatory, making it accessible to learners with basic computer skills. Join us to enhance your proficiency in R and excel in statistical computing and data analysis.

Details

Target audience

Digital skills for all

Digital skills for the workforce

Digital skills for ICT professionals

Digital technology

Web development

Software engineering

Big Data

Level

Basic

Format of the training

Online

Training fee

Free training

Duration of the training

Type of training

Language of the training

English

Country providing the training

Other

Classification

Single opportunity

MACHINE LEARNING SPECIALIZATION

By OPPORTUNITIES: Training, Course, Free training, Format of the training, Online training, English, Training fee, Other, Up to three months, Duration of training, Type of training, Language of the training, Country providing the training, OPPORTUNITIES
MACHINE LEARNING SPECIALIZATION

12.09.2023 |

The Machine Learning Specialization is a foundational online program created in collaboration between Stanford Online and DeepLearning.AI. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization, consisting of 3 courses, is an updated and expanded version of Andrew Ng’s original Machine Learning course. It offers a comprehensive introduction to modern machine learning, covering supervised learning (including multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (including clustering, dimensionality reduction, and recommender systems), and best practices in artificial intelligence and machine learning innovation (such as model evaluation, tuning, and data-centric performance improvement).

This course requires no prior knowledge, making it accessible to beginners. It covers core competencies including multiple linear regression, logistic regression, neural networks, decision trees, clustering, dimensionality reduction, recommender systems, and best practices for evaluating and tuning models, as well as adopting a data-centric approach to enhance performance. Get started on your machine learning journey with this accessible and comprehensive course.

Details

Target audience

Digital skills for all

Digital skills for the workforce

Digital skills for ICT professionals

Digital technology

Digital skills

Artificial Intelligence

Level

Basic

Format of the training

Online

Training fee

Free training

Duration of the training

Type of training

Language of the training

English

Country providing the training

Other

Classification

Single opportunity