機械学習

機械学習:Machine Learning

Protected: Explainable machine learning (1) Interpretable models (linear regression model)

Linear Regression Model as Explainable Machine Learning for Digital Transformation (DX), Artificial Intelligence (AI) and Machine Learning (ML)
Clojure

one hot vector and category vector with Clojure

Implementation of one-hot-vector and category-vector in Clojure for machine learning applications in natural language processing
最適化:Optimization

Protected: Continuous Optimization in Machine Learning – Overview

Explanation of the mathematical theory underlying optimization algorithms for machine learning.
機械学習:Machine Learning

General Machine Learning and Data Analysis

Overview of machine learning for beginners, differences between types of learning and results, no-free lunch theorem as a frame for machine learning.
python

Clustering with R – k-means

Machine learning using R, unsupervised, non-hierarchical classification using k-means
R

hierarchical clustering with R

Machine learning using R, classification with hierarchical clustering function hclust
R

R Language Preferences

Overview of machine learning using R, setting up the environment and handling data
R

Reference books on machine learning with R

Overview of machine learning with R, history of R and introduction to reference books
自然言語処理:Natural Language Processing

Openrefine: Data cleansing tool

Overview of openrefine, a natural language processing tool used in DX and the Semantic Web
Clojure

Sentence classification using liblinear and natural language processing

Classification in liblinear with Clojure, a natural language processing tool., liblinear, SVM, classification with Clojure
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