Towards Data Science

Anime Obsession gone too far!! OtakusHenry Chang, Joey Chen, Guanhua Zhang, Preetika Srivastava and Cherry Agarwal The vast amount of data that is hosted on the internet today has led … Read more

Why cleaning data is the most important step Original Project Mission: Find interesting insights to see where the remodeling market is headed Project Mission (Twist): How to handle well manicured … Read more

Every author dreams of writing full time, but the sad truth is that the majority of authors don’t make nearly enough to support themselves, let alone a family. If you … Read more

The first thing I like to do when doing EDA on a dataset with a reasonable amount of numeric columns, is to check the relationship between my target variable and … Read more

Tf-idf vectors with word-embeddings are analyzed for clustering effectiveness. The text corpus examples considered here indicate that custom word-embeddings can help improve clusterability of the corpus That is welcome news after … Read more

3rd Wave Data Visualization By Elijah Meeks — 12 min read Imagine what it was like to do data visualization 30 years ago. It’s 1988 and you’re using Excel 2.0 for simple … Read more

Executing standard SQL queries on your Amazon S3 bucket files Dec 14, 2018 “What’s Amazon Athena?”, I hear you ask. Good question. It’s one of Amazon Web Services’ amenities for architecture … Read more

“The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups (although you tend … Read more

Dec 14, 2018 It looks like Christmas is a little early this year 😉 Here’s a little something from me to all of you out there: a map to navigate ML … Read more

Time Series Forecasting using Auto-ARIMA in python. AI and future Currently, there is a lot of development going on in Artificial intelligence research to get an accurate glimpse of the future. If … Read more

Joseph Catanzarite The Naïve Bayes Classifier is perhaps the simplest machine learning classifier to build, train, and predict with. This post will show how and why it works. Part 1 … Read more

Plus our VGG-ResNet ensemble model with state-of-the-art results MNIST, a dataset with 70,000 labeled images of handwritten digits, has been one of the most popular datasets for image processing and … Read more

Alright it’s most definitely not useless. But it is far more useless than it needs to be. This article will outline some of the potential roles that data plays in … Read more

Imports, Data Cleansing, and EDA Cleaning and EDA are important for this challenge as this data set contains many ordinal / categorical features that may be important in categorization and will … Read more

Our faces and voices can be analyzed for emotion. As I mentioned, biomimicry, or imitating natural design in the things we create, is critical in recreating this human tendency in AI. … Read more

If you ever struggled with tuning Machine Learning (ML) models, you are reading the right piece. Hyper-parameter tuning refers to the problem of finding an optimal set of parameter values … Read more

Methodology Choose inputs and outputs. Collect and aggregate the data. Prepare the data. Explore and attempt to understand the data. Choose a Machine Learning Model. Measure the performance of the … Read more

Our Readers’ Guide We have received feedback that some of you find it difficult to efficiently navigate our Medium publication. So we have put together a few bullet points that will … Read more

we are going to have a quick look at the architecture of four different state-of-the-art approaches by referring to the actual research paper and then we will move on to … Read more

RNNs have inherent performance limitations For a while, it seemed that RNN’s were taking the Natural Language Processing (NLP) world by storm (from about 2014–17). However, we’ve recently started realizing … Read more

Machine Learning is the science (and art) of programming computers so they can learn from data. [Machine Learning is the] field of study that gives computers the ability to learn … Read more

Data science is a booming field, but with great publicity comes great difficulty. Breaking into the data science field gets twice as hard each year. The growth of training programs … Read more

The Electric Pulse Thomas Parker Electric Car (1895) | Fisker Karma (2012) The credit to who invented the first electric vehicle is also debated due to the fact that many scientists and … Read more

The Kernel Trick We have seen how higher dimensional transformations can allow us to separate data in order to make classification predictions. It seems that in order to train a support … Read more

User review networks for customer segmentation Over the past decade or two, Americans have continued to prefer payment methods that are traceable, providing retailers and vendors with a rich source … Read more

Computer Vision it at the forefront of advancements in Artificial Intelligence (AI). It’s moving fast with new research coming out each and every day allowing us to do truly amazing … Read more

50 ways to label data There are different ways to get your data labeled. Some firms label their data themselves — although this can be costly, as hiring people simply for these tasks … Read more

The debate of whether AI will ever achieve capabilities at par or beyond human intelligence is ever ongoing. It certainly has intensified with the recent advancements in AI, Machine Learning … Read more

https://initiatives.provost.uci.edu/event/philosophy-machine-learning-knowledge-causality/ I quit my job to enter an intensive data science bootcamp. I understand the value behind the vast amount of data available that enables us to create predictive machine … Read more

In this post, I’m going to review some of the top concepts I learned that turned me from a technical data scientist to a good data scientist Two months ago, … Read more

The AI future is uncertain, but generally, I think it will improve life. I was one of the 900+ futurists interviewed for The Pew Research study released yesterday, “Artificial Intelligence and … Read more

An explanation of Bayes’ theorem and linear discriminant analysis Photo by Jerry Kiesewetter on Unsplash Overview Previously, logistic regression was introduced for classification. Unfortunately, like any model, it presents some flaws: … Read more

The Undertaking Recently, I was involved in developing a machine learning solution for one of the largest North American steel manufacturers. The company wanted to leverage the power of ML … Read more

Select the probability threshold based on the ROC curve BigQuery provides an incredibly convenient way to train machine learning models on large, structured datasets. In an earlier article, I showed you … Read more

Q: How does a data scientist manage projects and teams? How do you make duration and resource estimations? These are great questions, and I think people don’t ask them enough, … Read more

A series of insights and battle scars from the world of medical device design With the upcoming launch of one of our AI products, there has been a repeating question that … Read more

Key Points of My Work Disclaimers: I will present only a portion of the code I wrote for this competition, my teammates are absolutely not responsible for my awful and buggy … Read more

You’ve heard a lot about student debt, but what about technical debt? It’s Friday evening in the Bahamas. You’re relaxing under a striped red umbrella with a succulent glass of … Read more

From silver bullet syndrome to silver linings In this article I plan to share with you our recent experience pitching AI to business folk, and what lessons we learned along the … Read more