-
Math for Machine Learning
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
-
Ensemble Learning: 5 Main Approaches
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
-
The Backpropagation Algorithm Demystified
A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.
-
Supervised Learning: Model Popularity from Past to Present
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
-
A Case For Explainable AI & Machine Learning
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
-
Deep learning in Satellite imagery
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
-
Feature Engineering for Machine Learning: 10 Examples
A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.
-
Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
-
How will automation tools change data science?
This article provides an overview of recent trends in machine learning and data science automation tools and addresses how those tools will change data science.
-
 Introduction to Statistics for Data Science
This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning.
|
Introduction to Statistics for Data Science