remove dead links
diff --git a/blogs.md b/blogs.md
index 9a0ece2f..5d47235b 100644
--- a/blogs.md
+++ b/blogs.md
@@ -16,7 +16,6 @@ Podcasts
 * [Linear Digressions](https://lineardigressions.com)
 * [Data Stories](http://datastori.es/)
 * [Learning Machines 101](https://www.learningmachines101.com/)
-* [Not So Standard Deviations](https://simplystatistics.org/2015/09/17/not-so-standard-deviations-the-podcast/)
 * [TWIMLAI](https://twimlai.com/shows/)
 * [Machine Learning Guide](http://ocdevel.com/podcasts/machine-learning)
 * [DataTalks.Club](https://anchor.fm/datatalksclub)
diff --git a/books.md b/books.md
index fb73c879..60d4f3c4 100644
--- a/books.md
+++ b/books.md
@@ -45,11 +45,9 @@ The following is a list of free and/or open source books on machine learning, st
 * [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning) - Early access book that introduces the most valuable machine learning techniques.
 - [Foundations of Machine Learning](https://cs.nyu.edu/~mohri/mlbook/) - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
 - [Understanding Machine Learning](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/) - Shai Shalev-Shwartz and Shai Ben-David
-- [How Machine Learning Works](https://www.manning.com/books/how-machine-learning-works) - Mostafa Samir. Early access book that intorduces machine learning from both practical and theoretical aspects in a non-threating way. 
 - [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data)  [Free Chapter] Carl Gold - Hands on course in applied data science in Python and SQL, taught through the use case of customer churn.
 - [Machine Learning Bookcamp](https://www.manning.com/books/machine-learning-bookcamp) - Alexey Grigorev - a project-based approach on learning machine learning (early access).
 - [AI Summer](https://theaisummer.com/) A blog to help you learn Deep Learning an Artificial Intelligence
-- [Python Data Science Handbook- Oriely](https://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf)
 - [Mathematics for Machine Learning](https://mml-book.github.io/)
 - [Approaching Almost any Machine learning problem Abhishek Thakur](https://github.com/abhishekkrthakur/approachingalmost)
 - [MLOps Engineering at Scale](https://www.manning.com/books/mlops-engineering-at-scale) - Carl Osipov - Guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers.
@@ -81,7 +79,6 @@ The following is a list of free and/or open source books on machine learning, st
 * [Machine Learning for Business](https://www.manning.com/books/machine-learning-for-business) Early access book
 * [Probabilistic Deep Learning with Python](https://www.manning.com/books/probabilistic-deep-learning-with-python) Early access book
 * [Deep Learning with Structured Data](https://www.manning.com/books/deep-learning-with-structured-data) Early access book
-* [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf)
 * [Deep Learning](https://www.deeplearningbook.org/)[Ian Goodfellow, Yoshua Bengio and Aaron Courville]
 * [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) 
 * [Inside Deep Learning](https://www.manning.com/books/inside-deep-learning) Early access book
diff --git a/courses.md b/courses.md
index b3eb292d..d2323301 100644
--- a/courses.md
+++ b/courses.md
@@ -5,7 +5,6 @@ The following is a list of free or paid online courses on machine learning, stat
 * [Artificial Intelligence (Columbia University)](https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-0) - free
 * [Machine Learning (Columbia University)](https://www.edx.org/course/machine-learning-columbiax-csmm-102x-0) - free
 * [Machine Learning (Stanford University)](https://www.coursera.org/learn/machine-learning) - free
-* [Neural Networks for Machine Learning (University of Toronto)](https://www.coursera.org/learn/neural-networks) - free. Also [available on YouTube](https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLYvFQm7QY5Fy28dST8-qqzJjXr83NKWAr) as a playlist. #This course is no longer available on Coursera.
 * [Deep Learning Specialization (by Andrew Ng, deeplearning.ai)](https://www.coursera.org/specializations/deep-learning) - Courses: I Neural Networks and Deep Learning; II Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; III Structuring Machine Learning Projects; IV Convolutional Neural Networks; V Sequence Models; Paid for grading/certification, financial aid available, free to audit
 * [Deep Learning Nano Degree on Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101) - $
 * [Intro to Deep Learning (MIT)](http://introtodeeplearning.com/)
diff --git a/ml-curriculum.md b/ml-curriculum.md
index b12cbb93..381c9add 100644
--- a/ml-curriculum.md
+++ b/ml-curriculum.md
@@ -23,6 +23,5 @@ While you work on your individual projects, I would maybe deepen your (statistic
 
 When you are through all of that and still hungry to learn more, I recommend
 
-- [The Deep Learning book](https://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow, and Aaron Courville. The release date is set around 2016, but the 613-page manuscript is already available as as of today (online and for free).
 
 - And in-between, if you are looking for a less technical yet very inspirational free-time read, I highly recommend [Pedro Domingo's The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World](https://homes.cs.washington.edu/~pedrod/)
