Introduction to Interactive Time Series Visualizations with Plotly in Python

Introduction to Plotly Plotly is a company that makes visualization tools including a Python API library. (Plotly also makes Dash, a framework for building interactive web-based applications with Python code). For this article, we’ll stick to working with the plotly Python library in a Jupyter Notebook and touching up images in the online plotly editor. When … Read moreIntroduction to Interactive Time Series Visualizations with Plotly in Python

The Ultimate NanoBook to understand Deep Learning based Image Classifier

The first and most important step of our journey: As I have said before, we are going to simply ask questions that will guide us to build an image classifier. For the sake of brevity, we will call Image Classifier an ICNow, we are ready to start our journey. So let us ask the first question: … Read moreThe Ultimate NanoBook to understand Deep Learning based Image Classifier

Applying GANs to Super Resolution

SRGAN Results from Ledig et al. [3] Generative adversarial networks (GANs) have found many applications in Deep Learning. One interesting problem that can be better solved using GANs is super-resolution. Super-resolution is a task concerned with upscaling images from low-resolution sizes such as 90 x 90, into high-resolution sizes such as 360 x 360. In this … Read moreApplying GANs to Super Resolution

A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way

Artificial Intelligence has been witnessing a monumental growth in bridging the gap between the capabilities of humans and machines. Researchers and enthusiasts alike, work on numerous aspects of the field to make amazing things happen. One of many such areas is the domain of Computer Vision. The agenda for this field is to enable machines … Read moreA Comprehensive Guide to Convolutional Neural Networks — the ELI5 way

Dealing With Class Imbalanced Datasets For Classification.

Skewed datasets are not uncommon. And they are tough to handle. Usual classification models and techniques often fail miserably when presented with such a problem. Although your model could get you to even a 99% accuracy on such cases, yet, if you are measuring yourself against a sensible metric such as the ROC Auc score, … Read moreDealing With Class Imbalanced Datasets For Classification.

Google Landmark Recognition using Transfer Learning

Image classification with 15k classes! Project by Catherine McNabb, Anuraag Mohile, Avani Sharma, Evan David, Anisha Garg Dealing with a large number of classes with very few images in many classes is what makes this task really challenging! The problem comes from a famous Kaggle competition, the Google Landmark Recognition Challenge. Training set contains over 1.2 … Read moreGoogle Landmark Recognition using Transfer Learning

Because it’s Friday: CGI you never knew was CGI

Computer-generated imagery in movies has gotten so good these days, much of the time you don’t even realize it’s there. You probably never noticed how Michael Cera’s physique had been altered, or how Lost in Translation used motion capture technology from the future. [embedded content] That’s all from the blog team for this week. Have … Read moreBecause it’s Friday: CGI you never knew was CGI

Anime Recommendation engine: From Matrix Factorization to Learning-to-rank

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 to the information overflow and thus there is a constant need to improve the user experience. A recommendation engine is a system that helps support … Read moreAnime Recommendation engine: From Matrix Factorization to Learning-to-rank

Introduction to Web Scraping with BeautifulSoup

Find specific elements in the page The created BeautifulSoup object can now be used to find elements in the HTML. When we inspected the website we saw that every list item in the content section has a class that starts with tocsection- and we can us BeautifulSoup’s find_all method to find all list items with that … Read moreIntroduction to Web Scraping with BeautifulSoup

Home Remodeling Analysis Turned Excel Data Handling in Python

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 excel data in Python (spoiler: neat is a deceptive word) Timeline: One week (I tell you, it’s not enough!) Project Findings for the Original Goal : … Read moreHome Remodeling Analysis Turned Excel Data Handling in Python

The complete guide for topics extraction with LDA (Latent Dirichlet Allocation) in Python

A recurring subject in NLP is to understand large corpus of texts through topic extraction. Whether you analyze users’ online reviews, product descriptions, or text entered in search bars, understanding key topics will always come in handy. Popular picture explaining LDA Before going into the LDA method, let me remind you that not reinventing the … Read moreThe complete guide for topics extraction with LDA (Latent Dirichlet Allocation) in Python

Exploratory Data Analysis, Feature Engineering and Modelling using Supermarket Sales Data. Part 1.

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 these numeric features. One quick way to do this is to use the seaborn heatmap plot. This seaborn heatmap takes the correlation matrix calculated on … Read moreExploratory Data Analysis, Feature Engineering and Modelling using Supermarket Sales Data. Part 1.

Want to Cluster Text? Try Custom Word-Embeddings!

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 our ho-hum results for text classification when using word-embeddings. In the context of classification we concluded that keeping it simple with naive bayes and tf-idf … Read moreWant to Cluster Text? Try Custom Word-Embeddings!

Weekly Selection — Dec 14, 2018

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 charts like pie charts and line charts, or maybe something like SPSS for more complicated exploration and Arc/Info for geospatial data visualization. Favorite

CSV Analysis with Amazon Athena

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 in the cloud. More specifically, Athena allows us to query data we hold in another service called Amazon Simple Storage Service (S3) using standard SQL … Read moreCSV Analysis with Amazon Athena

Using Analysis of Variance with Experimentation Data

“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 to only see it used when there are a minimum of three, rather than two groups)”. Having entered the world of digital analytics from a … Read moreUsing Analysis of Variance with Experimentation Data

Get a glimpse of future using time series forecasting using Auto-ARIMA and Artificial Intelligence

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 any mathematical model predicts future data taking input as only time then that terminology called as time series forecasting. There are many machine learning and … Read moreGet a glimpse of future using time series forecasting using Auto-ARIMA and Artificial Intelligence

The Naive Bayes Classifier

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 reveals that the much-celebrated Bayes Rule is just a simple statement about joint and conditional probabilities. But its blandness belies astonishing power, as we’ll see … Read moreThe Naive Bayes Classifier

Kuzushiji-MNIST – Japanese Literature Alternative Dataset for Deep Learning Tasks

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 classification for over twenty years. Despite its popularity, contemporary deep learning algorithms handle it easily, often surpassing an accuracy result of 99.5%. A new paper … Read moreKuzushiji-MNIST – Japanese Literature Alternative Dataset for Deep Learning Tasks

Learning Data Science on Generic Datasets is Useless

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 learning data science, with an argument against using generic (and static for that matter) datasets. All too often we see machine learning topics taught on … Read moreLearning Data Science on Generic Datasets is Useless

Closing the Sale: Predicting Home Prices via Linear Regression

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 need to be converted to numerical values. As a baseline, I imported the following libraries to clean, explore and model the training data. One of … Read moreClosing the Sale: Predicting Home Prices via Linear Regression

Nobody UNDERSTANDS Me … But Soon, Artificial Intelligence Just Might

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. Our end goal is artificial empathy, which (for now, at least) describes a machine’s ability to recognize and respond to human emotion. Going in line … Read moreNobody UNDERSTANDS Me … But Soon, Artificial Intelligence Just Might

Pandas for data.table Users

R and Python are both great languages for data analysis. While they are remarkably similar in some aspects, they are drastically different in others. In this post, I will focus on the similarities and differences between Pandas and data.table, two of the most prominent data manipulation packages in Python/R. There is alreay an excellent post … Read morePandas for data.table Users

An introduction to high-dimensional hyper-parameter tuning

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 for a learning algorithm. Usually, the process of choosing these values is a time-consuming task. Even for simple algorithms like Linear Regression, finding the best … Read moreAn introduction to high-dimensional hyper-parameter tuning

Preprocessing with sklearn: a complete and comprehensive guide

For aspiring data scientist it might sometimes be difficult to find their way through the forest of preprocessing techniques. Sklearn its preprocessing library forms a solid foundation to guide you through this important task in the data science pipeline. Although Sklearn a has pretty solid documentation, it often misses streamline and intuition between different concepts. … Read morePreprocessing with sklearn: a complete and comprehensive guide

IcoOmen: Using Machine Learning to Predict ICO Prices

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 Model. Save the Model. Use the Model to make predictions. 1. Choosing Inputs and Outputs Inputs Choosing the right inputs and outputs (in the case of … Read moreIcoOmen: Using Machine Learning to Predict ICO Prices

How to Predict Severe Traffic Jams with Python and Recurrent Neural Networks?

An Application of Sequence Model to Mine Waze Open Data of Traffic Incidents, using Python and Keras. In this tutorial, I will show you how to use RNN deep learning model to find patterns from Waze Traffic Open Data of Incidents Report, and predict if severe traffic jams will happen shortly. Interventions can be taken out … Read moreHow to Predict Severe Traffic Jams with Python and Recurrent Neural Networks?

How to get the most out of Towards Data Science?

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 hopefully aid your experience on our blog. Subscribe to our publication to receive our Monthly Edition and Weekly Selection directly in your mailbox. Follow us … Read moreHow to get the most out of Towards Data Science?

Vaex: Out of Core Dataframes for Python and Fast Visualization

So… no pandas ?? There are some issues with pandas that the original author Wes McKinney outlines in his insightful blogpost: “Apache Arrow and the “10 Things I Hate About pandas”. Many of these issues will be tackled in the next version of pandas (pandas2?), building on top of Apache Arrow and other libraries. Vaex starts … Read moreVaex: Out of Core Dataframes for Python and Fast Visualization

Music Genre Classification with Python

Objective Companies nowadays use music classification, either to be able to place recommendations to their customers (such as Spotify, Soundcloud) or simply as a product (for example Shazam). Determining music genres is the first step in that direction. Machine Learning techniques have proved to be quite successful in extracting trends and patterns from the large … Read moreMusic Genre Classification with Python

Named Entity Recognition (NER), Meeting Industry’s Requirement by Applying state-of-the-art Deep…

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 implement the one with the highest accuracy. Bidirectional LSTM-CRF: More details and implementation in keras. from the paper(Bidirectional LSTM-CRF Models for Sequence Tagging) 2. Bidirectional LSTM-CNNs: … Read moreNamed Entity Recognition (NER), Meeting Industry’s Requirement by Applying state-of-the-art Deep…

The Importance of Being Recurrent for Modeling Hierarchical Structure

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 the limitations of RNN’s, primarily that they are “inefficient and not scalable”. While there is great promise in overcoming these limitations by using more specialized … Read moreThe Importance of Being Recurrent for Modeling Hierarchical Structure

Text Summarization on the Books of Harry Potter

Hermione interrupted them. “Aren’t you two ever going to read Hogwarts, A History?” How many times throughout the Harry Potter series does Hermione bug Harry and Ron to read the enormous tome Hogwarts, A History? Hint: it’s a lot. How many nights do the three of them spend in the library, reading through every book … Read moreText Summarization on the Books of Harry Potter

Supervised Machine Learning: Classification

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 without being explicitly programmed. Arthur Samuel, 1959 A better definition: A computer program is said to learn from experience E with respect to some task … Read moreSupervised Machine Learning: Classification

7 Tips to Getting a Data Science Job Faster

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 like graduate degrees/certificates and bootcamps far exceeds the growth of new entry-level positions. Prior to 2015, it was a cake-walk to get multiple interviews. Now … Read more7 Tips to Getting a Data Science Job Faster

First Mile

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 tinkerers were working with various forms of electric sources (batteries and electric motors) around the same time. However, there is a prominent name in electric … Read moreFirst Mile

The Kernel Trick

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 vector classifier and optimize our objective function, we would have to perform operations with the higher dimensional vectors in the transformed feature space. In real … Read moreThe Kernel Trick

Amazon Customer Analysis

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 of data on their customers. This data is used by data scientists to help businesses make more informed decisions with respect to inventory, marketing, and … Read moreAmazon Customer Analysis

A Guide for Building Convolutional Neural Networks

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 things that we could’t do before with computers and AI. Convolutional Neural Networks (CNNs) are the driving force behind every advancement in Computer Vision research … Read moreA Guide for Building Convolutional Neural Networks

The invisible workers of the AI era

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 costs firms both money and flexibility. Other companies even find ways to get people to label their data for free. Ever wonder why Google’s reCAPTCHA … Read moreThe invisible workers of the AI era

AI and Machine Learning: Moving from Training to Education

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 (ML), and Deep Learning (DL) with some believing that the current technologies are already capable of paving the way for Artificial General Intelligence (AGI). You … Read moreAI and Machine Learning: Moving from Training to Education

Towards Ethical Machine Learning

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 learning algorithms. In addition to recognizing its value on a professional level, I benefit from these technologies as a consumer. Whenever I find myself in … Read moreTowards Ethical Machine Learning

How to give money to the R project

by Mark Niemann-Ross, an author, educator, and writer who teaches about R and Raspberry Pi at LinkedIn Learning I spend a LOT of time at r-project.org, in particular the sections for documentation and CRAN. But I hadn’t spent much time in the other areas: R Project, R Foundation, and links. When I recently wandered into the foundation area, … Read moreHow to give money to the R project

Parsing XML, Named Entity Recognition in One-Shot

Photo credit: Lynda.com Conditional Random Fields, Sequence Prediction, Sequence Labelling Parsing XML is a process that is designed to read XML and create a way for programs to use XML. An XML parser is the piece of software that reads XML files and makes the information from those files available to applications. While reading an … Read moreParsing XML, Named Entity Recognition in One-Shot

An introduction to web scraping with Python

Introduction As a data scientist, I often find myself looking for external data sources that could be relevant for my machine learning projects. The problem is that it is uncommon to find open source data sets that perfectly correspond to what you are looking for, or free APIs that give you access to data. In … Read moreAn introduction to web scraping with Python