Saturday, July 14, 2018

Machine Learning - Introduction

What is machine learning?

Machine learning, as a type of artificial intelligence (AI), enables computers to learn without being explicitly programmed, and to improve their functions when exposed to new data. By analyzing patterns in this data, the machine learning algorithms are self-adjusting based on a set of design rules.

An article in Medium

Machine Learning Explained

Machine Learnig Cheatsheet - SAS

How To Become A Machine Learning Engineer: Learning Path
Aug 19, 2017

Machine learning - Notes

Updated 15 July 2018,   24 June 2018,  6 October 2017,  23 August 2017, 30 July 2016

Thursday, July 12, 2018

Data Science - Online Study Notes and Video Courses - Free Also

About Data Science


You Need To Keep Learning In Data Science

10 Free Must-Read Books for Machine Learning and Data Science
April 2017 

Learn R Free

Edureka YouTube Video

Businesses Will Need One Million Data Scientists by 2018
International Data Corporation (IDC) predicts a need for 181,000 people with deep analytical skills in the US by 2018 and a requirement for five times that number of positions with data management and interpretation capabilities.

Data analytics is  growing. Now computer applications in industry are broughtly divided into transaction application and intelligence applications. Business intelligence, data mining, data analytics, data science etc. are the subjects that are in the area of intelligence applications of computers in business organizations.


Updated  14 Feb 2016, 7 Feb 2016

NPTEL IIT Madras Course: Introduction to Data Analytics

Harvard Stat 221 “Statistical Computing and Visualization”:  Online Lecture Links

Data Analysis
26 Resources 310+ Hours 24,298 Learners
Learn how to manipulate and analyze data better with this free online curriculum

The Open Source Data Science Masters
Curriculum for Data Science
Follow me on Twitter @clarecorthell   - Follow the author of this blog on   @knoltweet

The Open-Source Data Science Masters
The open-source curriculum for learning Data Science.
Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.

Updated  13 July 2018, 2 February 2018
14 Apr 2016,  14 Feb 2016

Thursday, February 1, 2018

Deep Learning - Introduction and Bibliography

Deep learning is a form of machine learning for nonlinear high dimensional data reduction
and prediction.

Using  Bayesian probabilistic perspective in deep learning provides a number of advantages. Specifically statistical interpretation and properties, more efficient algorithms for optimisation and
hyper-parameter tuning, and an explanation of predictive performance. Traditional high dimensional
statistical techniques; principal component analysis (PCA), partial least squares
(PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are shallow learners. Their deep learning counterparts exploit multiple layers of of data reduction which leads to performance gains. Stochastic gradient descent (SGD) training and optimisation
and Dropout (DO) provides model and variable selection. Bayesian regularization
is central to finding networks and provides a framework for optimal bias-variance trade-off
to achieve good out-of sample performance.

To illustrate the use of bayesian perspective,  an analysis of first time international bookings on Airbnb. is presented in the paper.

Deep Learning Introduction


How to get started with Deep Learning for Data Science?

-1. Learn Python and R ;)

0. Andrew Ng and Coursera


1. Siraj Raval: YouTube channel. Specifically this playlists:

- The Math of Intelligence:

- Intro to Deep Learning:

2. François Chollet's book: Deep Learning with Python (and R soon):


3. IBM Cognitive Class:


4. Medium blogs:


5. DataCamp:


Info collected from a Linkedin Post


Updated 2 February 2018
5 June 2017

Monday, September 18, 2017

IBM IoT - Products and Systems

Download the August 2017 Report by Aberdeen on IoT and Analytics: Better Manufacturing Decisions in the Era of Industry 4.0


Uploaded 19 September 2017


5 Sep 2017


25 August 2017


15 August 2017

Cognitive IoT is the use of cognitive computing technologies in combination with data generated by connected devices and the actions those devices can perform.

Get started with Watson Analytics

3 June 2016  uploaded



IBM wants to replace the spreadsheet with Watson Analytics

IBM Bluemix

What is Bluemix
The cloud platform powered by the world’s most popular open source projects

Products on Bluemix

Data and analytics
Application services
Internet of Things

Bluemix and Internet of Things

Experience a fully managed, cloud-hosted service designed to simplify and derive value from your IoT devices. See how companies are using Watson Internet of Things to transform their business from the inside out.
How it all fits together
Connect your device, send data to our cloud, set up and manage your devices, and use APIs to connect apps to your device data.

Start with your device – whether it’s a sensor, gateway, or something else – and let us help you connect it with one of our recipes.

Your device data is always secure when you connect to the cloud using open, lightweight MQTT messaging protocol or HTTP.
IBM Watson IoT Platform

The hub of the IBM IoT approach – set up and manage your connected devices so your apps can access live and historical data.

REST and real-time APIs
Use our secure APIs to connect your apps with data from your devices.

Your application and analytics
Create applications within IBM Bluemix, another cloud, or your own servers to interpret data.

How much will it (IBM Watson IoT Platform)  cost?

The IBM Watson IoT Platform charges on three metrics.

The average number of devices you connect over the month. You get 20 free devices a month with each plan.

The amount of data that devices exchange with the IBM Watson IoT Platform and associated applications.
You get free 100 MB data traffic a month with each plan (equivalent to 50,000 messages)
*assuming an average message size of 200 bytes

The amount of data stored in a historical database.
You get 1 GB free storage a month with each plan

Updated 20 September 2017,  20 September 2016,  24 March 2016

Monday, August 21, 2017

The Evolution of Internet of Things (IoT) - Developments in IoT

IoT Evolution Expo Las Vegas 2017

August 16, 2017


IoT Evolution

Cisco Keynote at IoT Evolution Expo Ft Lauderdale 2017
14 Mar 2017
Maciej Kranz of Cisco speaks at IoT Evolution Expo Ft Lauderdale 2017


Cisco Live 2016: The Business of IoT: Go Fast and Grow Fast Now
13 Jul 2016
The Business of IoT: Go Fast and Grow Fast Now - Rowan Trollope, Jahangir Mohammed, and Sandy Hogan.




A white paper on IoT evolution by Texas Instruments

Monday, July 31, 2017

Recent Books - IoT - Industry 4.0

E-Technologies: Embracing the Internet of Things: 7th International Conference, MCETECH 2017, Ottawa, ON, Canada, May 17-19, 2017, Proceedings

Esma Aïmeur, Umar Ruhi, Michael Weiss
Springer, 09-Jun-2017 - Computers - 319 pages

This book constitutes the refereed proceedings of the 7th International Conference on E-Technologies, MCETECH 2017, held in Ottawa, ON, Canada, in May 2017.
This year’s conference drew special attention to the ever-increasing role of the Internet of Things (IoT); and the contributions span a variety of application domains such as e-Commerce, e-Health, e-Learning, and e-Justice, comprising research from models and architectures, methodology proposals, prototype implementations, and empirical validation of theoretical models.

The 19 papers presented were carefully reviewed and selected from 48 submissions. They were organized in topical sections named: pervasive computing and smart applications; security, privacy and trust; process modeling and adaptation; data analytics and machine learning; and e-health and e-commerce.

Industry X.0: Realizing Digital Value in Industrial Sectors

Eric Schaeffer
Kogan Page Publishers, 03-May-2017 - Business & Economics - 192 pages

Industry X.0 takes an insightful look at the business impact of the Internet of Things movement on the industrial sphere. Eric Schaeffer combines deep analysis with practical strategic guidance, and offers tangible and actionable recommendations on how to realise value in the current digital age. Based on extensive research and insights into the six core competencies that have been identified by Accenture, Industry X.0 explores critical aspects of the Industrial Internet of Things (IIoT), discussing and defining them in an engaging and accessible manner. These include managing smart data, handling digital product development, skilling up the workforce, mastering innovation, making the most of platforms and ecosystems, and much more.

Meticulously researched and clearly explained, Industry X.0 makes a stringent case for companies to actively shift mind-sets away from products, towards services, value and outcomes. Complemented by a wealth of case studies and real world examples, this book provides invaluable, practical 'how-to' advice for business organizations as they embark on their journeys into the era of the IIoT.