The result of standardization (or Z-score normalization) is that the features will be rescaled so that they'll have the properties of a standard normal distribution with Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms. Intuitively, we can think of gradient descent as a prominent example (an optimization algorithm often used in logistic regression, SVMs, perceptrons, neural networks etc.); with features being on different scales, certain weights may update faster than others since the feature values play a role in the weight updates Other intuitive examples include K-Nearest Neighbor algorithms and clustering algorithms that use, for example, Euclidean distance measures – in fact, tree-based classifier are probably the only classifiers where feature scaling doesn't make a difference. In fact, the only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i some_val?"

Principal Component Analysis (PCA) is a simple yet popular and useful linear transformation technique that is used in numerous applications, such as stock market predictions, the analysis of gene expression data, and many more. In this tutorial, we will see that PCA is not just a "black box", and we are going to unravel its internals in 3 basic steps. The sheer size of data in the modern age is not only a challenge for computer hardware but also a main bottleneck for the performance of many machine learning algorithms. The main goal of a PCA analysis is to identify patterns in data; PCA aims to detect the correlation between variables. If a strong correlation between variables exists, the attempt to reduce the dimensionality only makes sense.

If these tasks were part of a bigger project, this gets checked off as well, and I get to see a motivational quote as a reward. Since I keep all of that in Dropbox, it is available across all my computers, and I don't have to worry about platform-specific workarounds. I know, this sounds all weird, but if there really is a person who is interested in this, I can elaborate more and upload an example to GitHub in no time. This article certainly became longer than I intended it to be. You probably didn't read all of it, but I hope that you at least skipped forward to this last section!

At present, predictive analytics is the most used form of AI in enterprise and companies are focusing on innovation, patenting their AI developments at a faster rate than ever before. Join us as we explore the rise of artificial intelligence in six charts including the top investors in AI and the most used AI enterprise solutions. As of June 2016, artificial intelligence received 974m of funding. This year's funding is set to surpass 2015's total and CB Insights suggests that 200 AI-focused companies have raised nearly 1.5 billion in equity funding. AI isn't limited to the business sphere, in fact the personal robot market, including'care-bots', could reach 17.4bn by 2020.

Hadoop distributor MapR has raised a new round of funding and may be preparing for an IPO next year, Salesforce acquires analytics startup BeyondCore, Coursera releases a new data analytics course together with PwC, and Apple CEO Tim Cook provided some illumination on how his company regards artificial intelligence (AI). We've got all the highlights in this Big Data Roundup for the week ending Aug. 21, 2016. Let's start with the news from Hadoop distributor MapR. The company recently announced that it has raised a round of equity financing worth 50 million, and provided a few select details about its financial performance. MapR is still a privately held company, so it can choose what to disclose and what not to disclose.

If you are interested in using the EnsembleClassifier, please note that it is now also available through scikit learn ( 0.17) as VotingClassifier. Here, I want to present a simple and conservative approach of implementing a weighted majority rule ensemble classifier in scikit-learn that yielded remarkably good results when I tried it in a kaggle competition. For me personally, kaggle competitions are just a nice way to try out and compare different approaches and ideas – basically an opportunity to learn in a controlled environment with nice datasets. Of course, there are other implementations of more sophisticated ensemble methods in scikit-learn, such as bagging classifiers, random forests, or the famous AdaBoost algorithm. However, as far as I am concerned, they all require the usage of a common "base classifier." In contrast, my motivation for the following approach was to combine conceptually different machine learning classifiers and use a majority vote rule.

Unsupervised learning, by contrast, is much harder. It is best thought of as a continuum between (a) the entire system being one gigantic, autonomous, self-learning machine and (b) solving certain problems within a much larger system that also involves humans and supervised learning techniques. For many enterprise solutions we are very close to (b). For personal assistants like Siri, we are a little closer to (a), but even in such applications true autonomous AI is still quite far away. Imagine the amount of human intervention that will need to happen on the back-end, or how many special cases must be handled by editors or trainers in teaching the system.

SAN DIEGO, CA--(Marketwired - Aug 15, 2016) - DB Networks, a leader in database cybersecurity, today announced that that it will be exhibiting at the NSA Information Assurance Symposium (IAS) from Aug. 16-18 in Washington, D.C., in booth number 724; and at the CyberTexas Conference from Aug. 23-24 in San Antonio, Texas, in booth number 110. At these upcoming events, DB Networks will hold booth demonstrations of the DBN-6300, an artificial intelligence (AI)-based database security appliance that non-intrusively discovers databases, immediately alerts when databases are under attack and pinpoints credentials that have been compromised. IT security teams are severely understaffed, and presently there's a shortage of more than 200,000 security professionals in the U.S. In addition, security operation centers (SOCs) are deluged with alerts each day and security personnel are able to respond to only a small fraction of the alerts. AI-based security solutions address these issues by being extremely accurate at identifying actual attacks, thus eliminating false positive alerts, and also by alleviating overworked staff from creating and maintaining white lists/black lists. DB Networks is dedicated to protecting mission critical databases through its patented AI technologies that utilize machine learning and behavioral analysis.

An Elon Musk-backed artificial intelligence research group just got a brand new toy from chip maker Nvidia. Nvidia nvda said on Monday that it had donated one of its new supercomputers to the OpenAI non-profit artificial intelligence research project. OpenAI debuted in December with financial backing from Tesla and SpaceX CEO Musk along with money from other high-profile technology luminaries like LinkedIn lnkd co-founder Reid Hoffman and PayPal pypl co-founder Peter Thiel. OpenAI's goal is partly to create a non-profit outside the corporate sector that could research artificial intelligence technologies without a financial incentive. The concern is that many companies like Google and Facebook that are researching artificial intelligence technologies would horde talent and only work on projects beneficial to their financial interests.

We have been tracking Amazon for years, but as a reference point consider that in November 2006 BusinessWeek ran a cover story with the title "Jeff Bezos' Risky Bet" where the concept of cloud computing as a business model disruptor was catapulted into the mainstream. Since then the risky bet has paid off for Amazon and has helped transform the business ecosystem in ways few could have predicted. Amazon considers cloud computing to be the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. Others may use different definitions but Amazon is the 500lb gorilla so for this post at least we will say we agree! The power of the AWS cloud is now driving continuous advancements in Analytics, Artificial Intelligence and IoT.