Visit the KDNuggets.com Statistics page and download a free outlier analysis boxplot template from the Topics section. Boxplot analysis can solve business analysis problems. This free…

Here is a great too for your AI research

I feel like a kid in a candy shop! 

If you are like me, and want to stay on top of AI, you spend a lot of time working through research papers

However, there is no tool which can show you great new research in AI in near real time

So, it was great to find this free and open source…

If you want some holiday reading, here is something for you 

I think  this is extraordinarily generous 

Applications of Deep Neural Networks 575 page free book by Jeff Heaton 

TOC below download links at the end of the post

Happy holidays :)  

0.1 Introduction . . . .…

Applications of Deep Neural Networks is a free 500 + page book by Jeff Heaton

The contents are as below

The download link is at the bottom of the page

Introduction                                               

 Python Preliminaries

• Assignments…

I am reading a very interesting paper called Principles and Practice of Explainable Machine Learning by

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI).

XAI is a complex subject…

I always seek good resources for my teaching at #universityofoxford and this is a great free book on Cloud native – a topic that is very much in the focus

The book needs a free registration and is provided as a full ebook by d2iq.

D2iq was formerly mesosphere – the commercial arm of mesos – but…

Free book - Artificial Intelligence: Foundations of Computational Agents

There are many excellent free books on Python – but Artificial Intelligence: Foundations of Computational Agents is about  a subject not commonly covered

I found the book useful as a introduction to Reinforcement Learning

As the title…

Hi all,

I'm writing to share a book I just published that I think many of you might find interesting or useful. 

The book is called "Machine Learning from Scratch." It provides complete derivations of the most common algorithms in ML (OLS, logistic regression, naive Bayes, trees, boosting, neural nets, etc.) both in theory and math. It also demonstrates constructions of each of these methods from scratch in Python using only numpy.



My aim with the…

Probability and Statistics- the terms which resonate together to create the vast applications of the fields of Data Science and Machine Learning, have immensely grown a huge followers’ base in this era. But programming the concept sometimes gets tricky and requires a lot of contemplation on the code. Allen B. Downey, in his ‘Think’ series has written a book to solve just the problem for everyone.…

Course material for Richard Weber's course on Probability for first year mathematicians at Cambridge. You can also check Richard's blog (a former colleague of my dad) here. It also includes exams question. This is the base material that needs to be mastered before being accepted in the prestigious Tripo III curriculum at Cambridge. The book (PDF) can be downloaded…

Originally posted here.

This article was written by Tom Fawcett and Drew Hardin.

Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation,…

I have been reading a book recently about algorithms in the wider sense

(40 algorithms every programmer should know -book and github link below)

I spend a lot of time with algorithms considering my teaching (AI at University of Oxford).

For Machine Learning and Deep Learning, we need to study a…

Recently Springer made some good books on maths free to download.

Competitive programming strategies are useful for many data science interviews and they help to improve your maths foundations.  There are not many books on this subject (although there are many good websites and YouTube resources).

So, I hope you find this book…

In various formats, one of the most frequent questions I am asked is the equivalent of:

“Can you recommend a free self-paced learning path for #machinelearning and #deeplearning?”

In this post, I attempt an answer

This is based on my work / teaching students primarily at Oxford University, but I…

One of the best-known books on statistics is now free

Larry Wasserman’s All of Statistics is free to download from Springer

I like this book because – unlike most books on statistics - it takes a modern approach to statistics and covers…

My new ebook about Agile, AI, data, deep learning, IT and much more.

Highly polemic, contrarian and insightful. It informs, educates and entertains. It incites thought, reflection and reassessment. It’s delightfully seductive, easy to read and hard to put down. And there’s a lot of it. So, you won’t be left…

This post is based on two insightful threads I read online (References below)

Based on these, I address the question of ‘The difference between Statistics and Data Science’. Traditionally, most people, including me, would say that ‘statistics came first and Data Science builds upon statistics’. This chain of thought is valid but as you see…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…