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 ### General Purpose Machine Learning
 * [SciPy](https://scipy.org/) - Fundamental algorithms for scientific computing in Python
-* [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.
@@ -105,7 +105,7 @@
 * [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">
@@ -291,8 +291,8 @@
 ## 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">