First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors propose a novel approach for hierarchical clustering of multivariate data. They construct cluster trees by estimating minimum volume sets using the q-One-Class SVM, and evaluate their method on a synthetic data set and two real word applications. While their new method seems to perform better than other approaches based on density estimation, I am not convinced by the benefits in practical applicability as the authors did not compare their method to the most commonly used hierarchical clustering techniques (agglomerative clustering with average linkage/ward). Minor comment: Rather than splitting their data once in a training and test set, the authors should perform 10-fold/5-fold cross-validation for a more reliable estimation of the generalizability of their method.

After completing this unit, you'll be able to: As we learned in the first unit, artificial intelligence is really a few ingredients that can be combined to meet your business needs. The reality is that most businesses are going to need more than one recipe; the challenges faced by marketing are very different than those seen by customer support. Let's take a look at how different parts of a business can use AI to improve their business outcomes. Do you have lots of potential customers, and need help getting through to them? Marketing is a great place for AI because companies usually have lots of data that can be used to target communications and send relevant messages.

In the last post, we talked about Topic Modeling or a way to identify several topics from a corpus of documents. The method used there was Latent Dirichlet Allocation or LDA. In this article, we're going to perform a similar task but through the unsupervised machine learning method of clustering. While the method is different, the outcome is several groups (or topics) of words related to each other. For this example, we will use the Wine Reviews dataset from Kaggle.

Clustering is a fundamental skill in your Data Science toolkit. It can solve a huge array of problems -- from user segmentation to anomaly detection -- and can help your team derive very interesting insights. Determining the right number of clusters for your project is a little more art than science. In this article, I will go over a few common ways to determine the right number of clusters. The objective of this metric is to find the "Elbow" of the WSS curve in order to determine the smallest number of clusters that captures the most amount of signal in your data.

Every great idea starts with a question. What if we were to…? Sometimes, the problem (especially if you're not a formally trained data scientist) is knowing what question to ask. And more importantly how to ask it. That's where design thinking comes in.

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized every aspect of our life and disrupted how we do business, unlike any other technology in the the history of mankind. Such disruption brings many challenges for professionals and businesses. In this article, I will provide an introduction to one of the most commonly used machine learning methods, K-Means. Machine learning is a scientific method that utilizes statistical methods along with the computational power of machines to convert data to wisdom that humans or the machine itself can use for taking certain actions. "It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention."

"Attracting Venture Capital for Dummies" is a best seller. The book states on page one, the Venture Capitalists (VCs) goal in life is to find cybersecurity unicorns. Much like a Cyndaquil Pokemon, unicorns have common traits and in order to attract VCs you must exhibit the commonalities of said unicorns. And for bonus points, require lots of data scientists. This is one of the two prerequisites that venture capitalists use to gauge Unicornness.