With the world changing so rapidly, every company or organization that adapts to the changes becomes an example for all others. One very recent example for all companies to learn from and implement in their own decision making is the transformation of RELX Group to a leading global information and analytics company. I mentioned in my previous article, and the…Continue
Added by Ronald van Loon on October 26, 2017 at 4:30pm — No Comments
Artificial intelligence or AI for short is the field of making computer think like humans by creating an artificial brain. Whatever the human can do intelligently is required to be moved into machines. The machine will just do what the human tells it and no more. For example, the human can sort numbers in an intelligent manner and so machines should be intelligent by sorting numbers like humans. To do this, there are a number of algorithms like bubble sort that allows the machine to think…Continue
Want to learn machine learning? Looking for data science tutorials and guides to help you master your data and produce actionable, game-changing insights?
Look no further than this list of machine learning eBooks from the Packt team....
Added by Richard Gall on July 21, 2017 at 6:00am — No Comments
Shahab Sheikh-Bahaei, Ph.D.*
Principal Data Scientist
Machine Learning (ML) is closely related to computational statistics which focuses on prediction-making through the use of computers. ML is a modern approach to an old problem: predictive inference. It makes an inference from “feature” space to “outcome/target” space.…Continue
Added by Shahab Sheikh-Bahaei on July 6, 2017 at 4:30pm — No Comments
Machine learning is cool. There is no denying in that. In this post we will try to make it a little uncool, well it will still be cool but you may start looking at it differently. Machine learning is not a black box. It is intuitive and this post is just to convey that.
If I give you this function
f(x) = x^2 + log(x) and ask you to tell me what will be
f(2), you will first laugh at me and then run away to do something important. This is trivial for you,…
Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…Continue
Though your impressions of machine learning may be colored by these mass media depictions, today’s algorithms are too application-specific to pose any danger of becoming self-aware. The goal of today’s machine learning is not to create an artificial brain, but rather to assist us in making sense of the world’s massive data stores.
The origins of machine learning –
Since birth, we are inundated with data. Our body’s sensors—the eyes, ears, nose, tongue, and…Continue
Added by Ankur Singh on December 30, 2016 at 9:52am — No Comments
Summary: The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant. Here we explore both sides of that argument.
Summary: Are there large, sustainable career opportunities in AI and if so where? Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI?
If you’re a data scientist thinking about expanding your career options into AI you’ve got a forest and…Continue
Added by William Vorhies on September 20, 2016 at 7:33am — No Comments
Summary: What are the earliest seeds of artificial intelligence? To whom do we owe thanks for starting us down this path? Many modern researchers to be sure, but the earliest is Leonardo Torres of Spain, in about 1914.
As data scientists it’s very cool to be at the forefront in this age of techno-optimism. Since the awakening of the digital age calculated by economic researchers to have begun about 1994, a wave of increased productivity has…Continue
Added by William Vorhies on September 6, 2016 at 7:06am — No Comments
Summary: Which of these terms means the same thing: AI, Deep Learning, Machine Learning? Are you sure? While there’s overlap none of these is a complete subset of the others and none completely explains the others.
Take this quiz.
Which of the following are substantially the same things?
B. Deep Learning
C. Machine Learning
(Select your answer)
1. A and B
2. B and C
3. A and…Continue
Here are three useful resources for learning about Data Science:
Added by Ujjwal Karn on May 18, 2016 at 8:59am — No Comments
As a follow-up to my previous post "Using Machine Learning to predict Customer Behaviour", I wanted to address a similar topic but from an e-commerce perspective. How to you predict the behaviour of your visitors in your online store? and more importantly, how do you leverage this knowledge in order to optimize your traffic, conversion, profit, or whatever KPI…Continue
Added by Alex Marandon on April 19, 2016 at 11:40pm — No Comments
Author: Marcos Sponton
A few comments for those who are about to invest on Machine Learning intensive project
During a conversation I had with Peter Norvig, we discussed about the kind of projects that we do at Machinalis and how strange does it feels to say that "we are a Machine Learning company": In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a…Continue
RATINGS ARE OVERRATED
‘Service is great, but the desserts are bad”. Overall rating 4.5/5.
Many times we have gone into a restaurant alone or in a group, came out happy and still rated it a 3 or a 4 on social media. Does any one check why 2 precious points were deducted? No one will read your review unless the overall rating dips below 3.5. Is it an healthy practice? Of course not.
Frankly, no one is to blame except the rating scales and NPS…
Added by Manas Ranjan Kar on December 28, 2015 at 7:57am — No Comments
Natural Language Processing (NLP) is a messy and difficult affair to handle. Preprocessing, machine learning, relationships, entities, ontologies and what not.
Word embeddings/representations – ever since they came in with great work of Mikolov et al, they have been revolutionary to say the least. The concept itself is very intuitive and motivates deeper understanding fora wide range of applications. The…Continue
Added by Manas Ranjan Kar on December 28, 2015 at 7:53am — No Comments