From 2eeaf162a7b7853a7bc959eb2bf3d7eb84f38b96 Mon Sep 17 00:00:00 2001
From: Joren Hammudoglu <jhammudoglu@gmail.com>
Date: Sun, 30 Nov 2025 08:41:26 +0100
Subject: [PATCH] replace `http://` with `https://`

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 0fdaf94..ba7a7ba 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@
 ## Machine Learning
 
 ### General Purpose Machine Learning
-* [scikit-learn](http://scikit-learn.org/stable/) - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [scikit-learn](https://scikit-learn.org/stable/) - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.  <img height="20" src="img/R_big.png" alt="R inspired lib">
 * [Shogun](https://github.com/shogun-toolbox/shogun) - Machine learning toolbox.
 * [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
@@ -104,7 +104,7 @@
 * [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <img height="20" src="img/keras_big.png" alt="keras"> <img height="20" src="img/tf_big2.png" alt="TensorFlow">
 
 ### Ensemble Methods
-* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [ML-Ensemble](https://ml-ensemble.com/) - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Stacking](https://github.com/ikki407/stacking) - Simple and useful stacking library written in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [stacked_generalization](https://github.com/fukatani/stacked_generalization) - Library for machine learning stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [vecstack](https://github.com/vecxoz/vecstack) - Python package for stacking (machine learning technique). <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -290,8 +290,8 @@
 ## Probabilistic Methods
 * [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
-* [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [GPflow](http://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [ZhuSuan](https://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
+* [GPflow](https://gpflow.readthedocs.io/en/latest/?badge=latest) - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [InferPy](https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.  <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyStan](https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
 * [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
@@ -512,7 +512,7 @@
 * [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
 
 ## Computations
-* [numpy](http://www.numpy.org/) - The fundamental package needed for scientific computing with Python.
+* [numpy](https://www.numpy.org/) - The fundamental package needed for scientific computing with Python.
 * [Dask](https://github.com/dask/dask) - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [bottleneck](https://github.com/kwgoodman/bottleneck) - Fast NumPy array functions written in C.
 * [CuPy](https://github.com/cupy/cupy) - NumPy-like API accelerated with CUDA.