chore(kb/ai): review and expand notes

knowledge base/ai/README.md

@@ -14,10 +14,11 @@ speech recognition, decision-making and language translation.
 
 ## Further readings
 
+- [Machine learning]
 - [Large Language Model] (LLM)
 - [Model Context Protocol] (MCP)
 - [Useful AI]: tools, courses, and more, curated and reviewed by experts.
-- geeksforgeeks.com's [Artificial Intelligence Tutorial][geeksforgeeks  artificial intelligence tutorial]
+- geeksforgeeks.com's [Artificial Intelligence Tutorial][geeksforgeeks/artificial intelligence tutorial]
 
 ### Sources
 
@@ -29,10 +30,11 @@ speech recognition, decision-making and language translation.
 <!-- In-article sections -->
 <!-- Knowledge base -->
 [Large Language Model]: llm.md
+[Machine learning]: ml.md
 [Model Context Protocol]: mcp.md
 
 <!-- Files -->
 <!-- Upstream -->
 <!-- Others -->
-[geeksforgeeks  Artificial Intelligence Tutorial]: https://www.geeksforgeeks.org/artificial-intelligence/
+[geeksforgeeks/Artificial Intelligence Tutorial]: https://www.geeksforgeeks.org/artificial-intelligence/
 [Useful AI]: https://usefulai.com/

knowledge base/ai/claude/claude code.md

@@ -205,7 +205,7 @@ Manually add the MCP server definition to `$HOME/.claude.json`:
 
 Refer [Skills][documentation/skills].
 
-See also [create custom skills].
+See also [create custom skills] and [Prat011/awesome-llm-skills].
 
 Skills superseded commands.<br/>
 Existing `.claude/commands/` files will currently still work, but skills with the same name will take precedence.
@@ -307,6 +307,7 @@ Claude Code version: `v2.1.41`.<br/>
 - [Claude Code router]
 - [Gemini CLI]
 - [OpenCode]
+- [Prat011/awesome-llm-skills]
 
 ### Sources
 
@@ -339,3 +340,4 @@ Claude Code version: `v2.1.41`.<br/>
 [AWS API MCP Server]: https://github.com/awslabs/mcp/tree/main/src/aws-api-mcp-server
 [Cost Explorer MCP Server]: https://github.com/awslabs/mcp/tree/main/src/cost-explorer-mcp-server
 [pffigueiredo/claude-code-sheet.md]: https://gist.github.com/pffigueiredo/252bac8c731f7e8a2fc268c8a965a963
+[Prat011/awesome-llm-skills]: https://github.com/Prat011/awesome-llm-skills

knowledge base/ai/ml.md

@@ -0,0 +1,102 @@
+# Machine learning
+
+Branch of [AI] focusing on developing models and algorithms that can learn patterns from data without being explicitly
+programmed for every task, and subsequently make accurate inferences about new data.
+
+It is a pattern recognition ability that enables models to make decisions or predictions without explicit, hard-coded
+instructions.
+
+<!-- Remove this line to uncomment if used
+## Table of contents <!-- omit in toc -->
+
+1. [TL;DR](#tldr)
+1. [Further readings](#further-readings)
+   1. [Sources](#sources)
+
+## TL;DR
+
+All machine learning is AI, but not all AI is machine learning.
+
+Rules-based models become increasingly brittle the more data is added to them.<br/>
+They require accurate, universal criteria to define the results they need to achieve. This is not scalable.
+
+ML models operate by a logic that is learned through experience, and not explicitly programmed into them.<br/>
+They train by analyzing data and predicting the next result; prediction errors are calculated, and the algorithm is
+adjusted to reduce the possibility of errors.<br/>
+The training process is repeated until the model is accurate.
+
+ML works through mathematical logic. Relevant characteristics (A.K.A. _features_) of each data point **must** be
+expressed numerically, so that the data can be fed into the mathematical algorithm that will _learn_ to map a given
+input to the desired output.
+
+ML is mainly divided into the following types:
+
+- _Supervised_ learning.
+
+  Models learn from _labelled_ data. Every input has a corresponding _correct_ output.<br/>
+  Models make predictions, compare those with the true outputs, and adjust themselves to reduce errors and improve
+  accuracy over time.<br/>
+  The goal is to train to make accurate predictions on new, unseen data.
+
+- _Unsupervised_ learning.
+
+  Models work **without** labelled data.<br/>
+  They learn patterns on their own by grouping similar data points or finding hidden structures without human
+  intervention.<br/>
+  Helps identify hidden patterns in data. Useful for grouping, compression and anomaly detection.<br/>
+  Used for tasks like clustering, dimensionality reduction and Association Rule Learning.
+
+- _Reinforcement_ learning.
+
+  Teaches agents to make decisions through trial and error to maximize cumulative rewards.<br/>
+  Allows machines to learn by interacting with an environment and receiving feedback based on their actions. This
+  feedback comes in the form of rewards or penalties.<br/>
+  Agents use the feedback to optimize their decision-making over time.
+
+- _Semi-supervised_ learning.
+
+  Hybrid machine learning approach using both supervised and unsupervised learning.<br/>
+  Uses a small amount of labelled data, combined with a large amount of unlabelled data to train models.<br/>
+  The goal is to learn a function that accurately predicts outputs based on inputs, like with supervised learning, but
+  with much less labelled data.<br/>
+  Particularly valuable when acquiring labelled data is expensive or time-consuming, yet unlabelled data is plentiful
+  and easy to collect.
+
+- _Self-supervised_ learning.
+
+  Subset of unsupervised learning.<br/>
+  Models train using data that does not have any labels or answers provided. Instead of needing people to label the
+  data, the models themselves find patterns and create their own labels from the data automatically.<br/>
+  Especially useful when there is a lot of data, but only a small part of it is labelled or labelling the data would
+  take a lot of time and effort.
+
+_Deep learning_ has emerged as the state-of-the-art AI model architecture across nearly every domain.<br/>
+It relies on distributed _networks_ of mathematical operations providing the ability to learn intricate nuances of very
+complex data.<br/>
+It requires very large amounts of data and computational resources.
+
+## Further readings
+
+### Sources
+
+- geeksforgeeks.com's [Machine Learning Tutorial][geeksforgeeks / machine learning tutorial]
+- IBM's [What is machine learning?][ibm / what is machine learning?]
+- Oracle's [What is machine learning?][oracle / what is machine learning?]
+- [Machine learning, explained]
+
+<!--
+  Reference
+  ═╬═Time══
+  -->
+
+<!-- In-article sections -->
+<!-- Knowledge base -->
+[AI]: README.md
+
+<!-- Files -->
+<!-- Upstream -->
+<!-- Others -->
+[geeksforgeeks / Machine Learning Tutorial]: https://www.geeksforgeeks.org/machine-learning/
+[IBM / What is machine learning?]: https://www.ibm.com/think/topics/machine-learning
+[Oracle / What is machine learning?]: https://www.oracle.com/artificial-intelligence/machine-learning/what-is-machine-learning/
+[Machine learning, explained]: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained