In-depth guide to machine learning in the enterprise

Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology.

Machine learning for enterprise use is exploding. From improving customer experience to developing products, there's almost no area of the modern business untouched by machine learning.

Machine learning is a pathway to creating artificial intelligence, which in turn is one of the primary drivers of machine learning use in the enterprise. There is some disagreement over the exact nature of the relationship between AI and machine learning. Some see machine learning as a subfield of AI, while others view AI essentially as a subfield of machine learning. In general, AI aims to replicate some aspect of human perception or decision-making, whereas machine learning can be used to enhance or automate virtually any task, not just ones related to human cognition. However you view them, the two concepts are closely linked, and they are feeding off each other's popularity.

The practice of machine learning involves taking data, examining it for patterns and developing some sort of prediction about future outcomes. By feeding an algorithm more data over time, data scientists can sharpen the machine learning model's predictions. From this basic concept, a number of different types of machine learning have developed:

Supervised machine learning

Unsupervised machine learning

Semisupervised machine learning

Reinforcement learning

From these four main types of machine learning, enterprises have developed an impressive array of techniques and applications. Everything from relatively simple sales forecasting to today's most cutting-edge AI tools run on machine learning models. This guide to machine learning in the enterprise explores the variety of use cases for machine learning, the challenges to adoption, how to implement machine learning technologies and much more.

Full Guide

https://searchenterpriseai.techtarget.com/In-depth-guide-to-machine-learning-in-the-enterprise

16 analytic disciplines compared to data science

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on?

Here the author of the post compares several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. He also provided typical job titles, types of analyses, and industries traditionally attached to each discipline.

Comparison with other analytic disciplines

Machine learning
Data mining
Predictive modeling
Statistics
Industrial statistics
Mathematical optimization.
Actuarial sciences
HPC
Operations research
Six sigma
Quant
Artificial intelligence.
Computer science
Econometrics
Data engineering
Business intelligence
Data analysis
Business analytics

Read the full article

Google AI algorithm masters ancient game of Go

Deep-learning software defeats human professional for first time.

A computer has beaten a human professional for the first time at Go — an ancient board game that has long been viewed as one of the greatest challenges for artificial intelligence (AI).

The best human players of chess, draughts and backgammon have all been outplayed by computers. But a hefty handicap was needed for computers to win at Go. Now Google’s London-based AI company, DeepMind, claims that its machine has mastered the game.

DeepMind’s program AlphaGo beat Fan Hui, the European Go champion, five times out of five in tournament conditions, the firm reveals in research published in Nature on 27 January1. It also defeated its silicon-based rivals, winning 99.8% of games against the current best programs. The program has yet to play the Go equivalent of a world champion, but a match against South Korean professional Lee Sedol, considered by many to be the world’s strongest player, is scheduled for March. “We’re pretty confident,” says DeepMind co-founder Demis Hassabis.

“This is a really big result, it’s huge,” says Rémi Coulom, a programmer in Lille, France, who designed a commercial Go program called Crazy Stone. He had thought computer mastery of the game was a decade away.

Read the story published in Nature

Why and How this Indian Realty Portal Exploits Data Science

While banks and ecommerce companies have tried their hands at data science and are benefitting from their initiatives around it, real estate appears to be the next big vertical that intends to leverage data science.

Housing.com, the Mumbai-based startup, is one among the top Indian online businesses that bet on data science and machine learning algorithms as a core priority. The realty portal, which raised $190 mn from Japan’s SoftBank, has come up with many tools such as Traffic Flux, Heat Maps, Listing Decay, and more in their efforts to present information to users in a visually appealing way that is more interactive than traditional plain listings.

Read the article

Deep Machine Learning libraries and frameworks

At the end of 2015, all eyes were on the year’s accomplishments, as well as forecasting technology trends of 2016 and beyond. One particular field that has frequently been in the spotlight during the last year is deep learning, an increasingly popular branch of machine learning, which looks to continue to advance further and infiltrate into an increasing number of industries and sectors. Here are a list of Deep Learning libraries and frameworks that will gain momentum in 2016.

1. Theano is a python library for defining and evaluating mathematical expressions with numerical arrays. It makes it easy to write deep learning algorithms in python. On the top of the Theano many more libraries are built.

· Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU.

· Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning. Its functional libraries are built on top of Theano

· Lasagne is a lightweight library to build and train neural networks in Theano. It is governed by simplicity, transparency, modularity, pragmatism , focus and restraint principles.

· Blocks a framework that helps you build neural network models on top of Theano.

Know the rest 29 items

An executive’s guide to machine learning

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.

Read the post

Machine Learning Lectures by Professor Andrew Ng, Stanford CS Dept.

This course (CS229) -- taught by Professor Andrew Ng -- provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. This is a series of 20 lectures of about 75 minutes each.Watch them