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Is your Distribution’s assumption correct? Let’s find it out. Photo by Luke Cheeser on Unsplash Every day we come across a variety of Data like Sensor Data, Sales Data, Customer Data, Traffic Data, etc. Further, depending on the use case, we do a variety of processing and try out several algorithms on it. Have you … Read more Identify your Data’s Distribution

If things go wrong with an AI project, the best case is usually wasted investment or missed opportunity. For example, failing to keep up with competitors. In worse cases, AI may inflict damage to some aspect of your business. Casualties can include sales growth, customer satisfaction, brand or operational efficiency. Business members of the AI … Read more 5 AI Pitfalls for Business & How to Avoid Them

Let’s build a simple serverless workflow using AWS services! I found a cool website (https://covid19api.com/) where we can easily access COVID19 data using free API. This gave me an idea to create simple function to grab the data using AWS Lambda and save it to S3. The script will be executed daily automatically using CloudWatch. … Read more How to use AWS Lambda and CloudWatch for beginners

Good day reader,I hope everyone is staying safe and washing their hands. It’s really during times like these where mental and physical health is extremely important to keep us going. As I mentioned in my previous article,data science does not crash with the economy.The data industry is still in demand, some would even argue it … Read more How to Create a Data Science Portfolio — by a Data Scientist

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below: Data science exists at the intersection of a number of genuinely technical topics, from statistics to programming to machine learning. … Read more Building a Career in Data Science with Emily Robinson

9 ways machine learning helps us fight the viral pandemic Viral pandemics are a serious threat. COVID-19 is not the first, and it won’t be the last. But, like never before, we are collecting and sharing what we learn about the virus. Hundreds of research teams around the world are combining their efforts to collect … Read more Fight COVID-19 with machine learning

Supercharge your data science learning journey I just finished Ultralearning by Scott Young, and I thought that the concepts in this book could help many people who are looking to learn data science. Scott used this approach to learn the entire MIT undergrad computer science coursework in a single year (it usually takes four) and … Read more How to ULTRALEARN Data Science

Hey Folks! Let me tell you the reason why I am writing an article on Supervised vs Unsupervised Learning. Photo by Sean Lee on Unsplash During my first internship, I was assigned the position of a Junior Coach at an E-learning platform. And while solving the doubts of Machine Learning I came across the fact … Read more Supervised v/s Unsupervised

Photo by Dan Freeman on Unsplash So in this tutorial, we’ll be making a heatmap of the most dangerous countries to drive in, as measured by the number of traffic deaths per 100,000 residents. We’ll use R and ggplot2 to visualize our results. Is It Really That Dangerous To Drive in Thailand? It’s often said … Read more Tutorial: ggplot2 Heatmaps and Traffic Deaths in Thailand

Community finding algorithms in real networks Zachary’s karate club network with 2 communities identified. Due to the size of real networks, it is sometimes unfeasible to use brute-force algorithms to define communities. Algorithms used to handle these problems, in the best-case scenario, run in polynomial time. Although, most of the times, it is necessary to … Read more Algorithmic Complexity

A step towards making ML research open and accessible Photo credit: geralt via Pixabay The NeurIPS (Neural Information Processing Systems) 2019 conference marked the third year of their annual reproducibility challenge and the first time with a reproducibility chair in their program committee. So, what is reproducibility in machine learning? Reproducibility is the ability to … Read more Reproducible Machine Learning

Looping over all States I wanted to construct a loop that would allow me to iterate over all states and concatenate all the results into one master dataframe. I started by creating a ‘master’ dataframe upon which I could append the other states, versus creating a blank data frame to ensure the column order would … Read more Scraping US Census Data via CenPy

The big section. Operators We can use all the equality operators you’d expect in a where clause: =, <>, !=, <, <=, >=, > . Find all records where the name is exactly “Liam”. select * from users where first_name = ‘Liam’ Find all records where the name is not “Liam”. select * from users … Read more SQL Query Cheatsheet for Postgres

Photo by Fabien Bazanegue on Unsplash The world just changed. For anyone in Tech, that’s a problem. The tech industry leads the world through innovation, and innovation can only happen when you’re able to predict demand and develop the solutions that meet it. That suddenly got a lot harder to do. It’s not easy to … Read more After Covid, AI will Pivot

At large Perplexity This is the sixteenth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. In my previous post, tSNE vs. UMAP: Global Structure, I touched the limit of large perplexity as … Read more tSNE Degrades to PCA

As with the UK and the rest of the world, we have been observing the impacts of COVID-19 as the pandemic continues to unfold, with a continuous rise in cases and deaths. This was one of the headlines in the news following the government’s request for volunteers once the UK lockdown was imposed by Boris … Read more Where are the most vulnerable people in the UK?

Improving ULMFiT with the right kind of attention In this article you can find: an introduction describing core ideas, history and applications of Transfer Learning, a review of recent developments in natural language processing — including the ULMFiT algorithm (utilizing pre-trained language models based on recurrent neural networks), a novel network architecture — modification of … Read more Explainable, data-efficient text classification

Our dear planet is enrichened by more than 7,000 languages, and thanks to Technology, we live in a world that is more and more globalized. Translation has become a pillar of communication allowing people to make all sorts of connections. To really get the importance of translations, here is a key figure: in 2015 Google … Read more Introduction to Machine Translation

TLDR; This post provides an E2E Jupyter notebook for training crowd counting models with C-3-Framework and AzureML SDK for Python Often as AI Cloud Developer Advocate I find myself talking in front of various audiences. Now that I have some time to work on some new demos while working from home I’ve been thinking of … Read more Attendance Estimation with Azure ML

In this day and age, cloud computing power is prevalent and cheap. One doesn’t need to look very hard online to find free or affordable hosting options for app development, databases, or data science projects. Regardless of online-availability, there are many reasons, like security, expenses and curiosity, to set up custom environments on your own … Read more Create a Free Linux Virtual Machine on your Computer for Data Science Projects using VirtualBox…

Scrape the tables Now, we are going to scrape those tables from the page using Beautifulsoup. The standard way of getting all the tables from the page is, page = requests.get(URL)soup = BeautifulSoup(page.content, ‘html.parser’)tables = soup.find_all(“table”) where requests.get(URL) is basically getting the information from the page and BeautifulSoup(page.content, ‘html.parser’) is to parse the information. We … Read more Scrape Tabular Data with Python

Advancements in CPU and GPU technology, easier access to large data repositories, and the convolutional neural network have led to great leaps in the field of computer vision. From facial recognition to cancer detection, CNN-based frameworks have the potential to benefit human society in countless ways. In this article, we introduce 5 papers on CNNs … Read more 5 Papers on CNNs Every Data Scientist Should Read

This month’s Data Science reading list. Note: I am not affiliated with any of the authors in this article. These are simply books I’ve recently enjoyed that I’m excited to share with you. There are no referrals or a cent going in my pocket. Enjoy! Welcome to this week’s reading list! The themes for this … Read more Data Science Reading List for April 2020

Marrow @ IDFA 2018_Annegien van Doorn © 2018 NFB. All rights reserved Back then, we worked with a specific conditional model of GAN — Pix2Pix — This model studies pairs of images, learning how to generate a destination image when being presented with a source. It can handle small datasets, which makes a lot of … Read more Small family: Small dataset

Training A Generative Text Model on Frank Herbert’s Dune Image by Greg Montani from Pixabay Dune is the story of a feudal society in the far distant future. It focuses on a Duke and his family, who have been forced to become stewards of the desert planet, Arrakis. Frank Herbert published this classic in 1965. … Read more The Text Must Flow

The plotting library we’ll be using is ggplot2. Based on the grammar of graphics, it operates on the idea that every graph can be built from three components: A data set A coordinate system Geoms, or visual marks that represent data points To display values, the variables in the data need to be mapped to … Read more Tutorial: Plotting in R for Python Refugees

Machine learning isn’t a “Skynet or bust” proposition Source: Pexels Disclaimer: My observations are influenced by the fact that I work on Cortex, an open source machine learning deployment platform. Every few weeks, a new article predicting an imminent AI winter gets circulated. The arguments generally follow the same lines: The power of deep learning … Read more There won’t be an AI winter this time

Photo from pixabay.com The latest OpenCV includes a Deep Neural Network (DNN) module, which comes with a nice pre-trained face detection convolutional neural network (CNN). The new model enhances the face detection performance compared to the traditional models, such as Haar. The framework used to train the new model is Caffe. However, some people have … Read more Extracting Coefficients of OpenCV Face Detection DNN model

A quick overview of integrating SQL queries to your Pandas DataFrames Photo by Ilona Froehlich on Unsplash Managing your data in a comprehensible and organized fashion is an absolute essential skill for any data scientist or analyst. Pandas DataFrames allow for clean views and operations on very large datasets, while it also is perhaps one … Read more How to Teach Your Panda SQL in 10 Minutes

Could you pick it out? It’s difficult! 😅 If you’d like to replicate the above experiment, the code is here. I like to use this example as a visual when introducing the Random Walk Theory because initially, it seems absurd — until you realize just how incredibly random numbers can resemble stock prices. Random numbers … Read more Can a Monkey Do Just as Well in the Stock Market as a Technical Analyst?

The pure apprehension of two density-based algorithms: DBSCAN and OPTICS Image credit: Fabrice Jazbinsek Clustering methods like partitional methods or hierarchical clusters are not effective in finding clusters of arbitrary shapes. The density-based clustering method is efficient in finding the clusters of arbitrary shapes also prevents outliers and noise. Object clustering when using a density-based … Read more Density-based algorithms

Simple Example: We have a 3-dimensional vector x = (x1, x2, x3). We define this operation f(x) as such: f(x) = (x1x1, x1x2, x1x3, x2x1, x2x2, x2x3, x3x1, x3x2, x3x3). In other words, it wants to multiply every pairs in x, and produce a 9-dimensional vector. Let’s plug it in some numbers and make it … Read more An Intuitive Explanation of Kernels in Support Vector Machine (SVM)

Generative and discriminative models are widely used machine learning models. For example, Logistic Regression, Support Vector Machine and Conditional Random Fields are popular discriminative models; Naive Bayes, Bayesian Networks and Hidden Markov models are commonly used generative models. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains like joint … Read more Generative vs Discriminative Probabilistic Graphical Models

Dominos as an analogy for dependencies, pixabay.com. Reasons for monitoring your model dependencies. In my previous article “Monitor! Stop Being A Blind Data Scientist”, I mentioned the many use cases and the monumental importance of monitoring & alerts on our field, specifically from a data-science-researcher point-of-view. I looked at use-cases and reviewed several companies that … Read more Monitor Your Dependencies! Stop Being A Blind Data-Scientist.