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Monte Carlo Cost Estimates: Engineers Throwing Dice
Estimating the cost of a complex project is not a trivial task. Traditional cost estimates are full of assumptions about the future state of the market and the final deliverable. Monte Carlo cost estimates are a tool to better understand the risks in your project and enable better cost control. Monte Carlo simulations are a technique to control your “known unknowns”. Albert Einstein famously said (…)
Simulating Monopoly: Finding the Best Properties Using MATLAB
I simulated 10 million Monopoly turns using MATLAB to find the best properties Image provided by the author Introduction: With 40 spaces, and 28 different properties, Monopoly may seem completely random at first, but there are certain properties that can be more beneficial to you over the course of a game. I wanted to find which properties these were using a MATLAB simulation of 10,000,000 (…)
What time-weighted averages are and why you should care
How to calculate time-weighted averages and how to use them for data analysis Image by National Cancer Institute on Unsplash Learn how time-weighted averages are calculated, why they’re so powerful for data analysis, and how to use TimescaleDB hyperfunctions to calculate them faster — all using SQL. Many people who work with time-series data have nice, regularly sampled d a tasets. Data could be (…)
8 Things you don’t know about Pandas groupby
Intro Datasets will rarely come to a Data Scientist’s hands the way we need it. Transformations are needed and aggregations are inevitable. That is why you must use groupby() to aggregate your data and be able to extract the wanted insights from it. Aggregations are needed to compact your data. Why would I need to look at 100 rows of the same category if I could group it and make it a single one, (…)
With MAPIE, uncertainties are back in machine learning !
Uncertainties are back in machine learning Presenting MAPIE, a scikit-learn-compatible package that allows you to easily estimate uncertainties associated with your favourite ML model After 25 years of success stories, modern machine learning (ML) is still at odds with the notion of uncertainty. Confidence intervals are not trendy and almost inexistent from top open-source libraries dedicated to (…)
Flask vs Django: Which Should You Learn?
Looking for a Flask vs Django comparison? This article compares the two popular Python frameworks for web, apps, APIs, and backend development. Read the full story
NLP Tutorial: Topic Modeling in Python with BerTopic
@davisdavid Davis David Data Scientist | AI Practitioner | Software Developer. Giving talks, teaching, writing. Topic modeling is an unsupervised machine learning technique thaat automatically identifies different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, (…)
How Big Data Carried Graph Theory Into New Dimensions
graph theory Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data. Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks — vertices (dots) and edges (lines connecting them) — has been an invaluable way to model real-world phenomena since at least the 18th (…)
Use HTML Templates for Smarter Web Development
Learn how to take a handful of static HTML files and convert them into templated files that will help you minimize errors and work more efficiently. Read the full story
No-Code Applications for AI/ML
Artificial intelligence and machine learning today allow us to reduce the time demanded for solving a certain problem from months and years to minutes. For example, it took your grandma years of practice to learn the right temperature for growing her zucchinis. If she had AI, she would do it much faster. But the problem is that even today not so many people can program ― only 0,5% ! This is where (…)
Machine learning explained at five difficulty levels
For their 5 Levels series, Wired brought in Hilary Mason to explain machine… Tags: Hilary Mason , machine learning , Wired
learning about unit charts
Last month we challenged you to tackle unit charts . The results were diverse with submissions including waffle charts, all sorts of icons, hexagons, timelines, and more. Many of you commented that this was your first time creating a unit chart, which is not surprising because unit charts aren’t frequently used in the data visualization world. Still, they can be a powerful tool to help your (…)
Doing ML Model Performance Monitoring The Right Way
The development and deployment of machine learning models enable artificial intelligence applications to solve problems. The underlying algorithms learn patterns from data, but the world constantly changes, so data also keeps changing. This means that ML algorithms have to keep up with a constant stream of new, changing data, and be regularly updated in order […] The post Doing ML Model (…)
Julia: A New Age Data Science
Julia: A New Age of Data Science Learn why Julia is future of data science and write your first Julia code Cover photo by Starline | Freepik Introduction Julia is a high-level and general-purpose language that can be used to write code that is fast to execute and easy to implement for scientific calculations. The language is designed to keep all the needs of scientific researchers and data (…)
Extensive Developer Road Maps for All
Naina Chaturvedi Aug 22 · 4 min read Data Science, ML, Front end, Back end, DevOps and many more… Pic Credits : Unsplash.com These roadmaps will help you out in your career as a developer and let you build a clear understanding what you need to learn/know next ( Credits below). Hacker Rank Analyzed Data from 100K+ Developers and Hiring Managers — Here is what I found Great Analysis Results from (…)
An Automatic Hyperparameter Optimization on a Twitter Sentiment Analysis Problem
This is an explanation of a nifty hyperparameter tuning technique to make your life easier. Feel free to use it in your next ML project! Long short-term memory neural network architecture is popular in the domain of Natural Language Processing as it has the capability to retain the sequence information in its “memory”. Just like XGBoost, we should vectorize the text data in order to train the (…)
Creating Charts in Google Slides with Python
Impress your audience leveraging Google’s API and the gslides package A common gap data scientists run up against is how to programmatically create simple, elegantly formatted and company-branded visualizations in a slide deck. Leveraging Google APIs and the package gslides you can easily create charts and tables in Google Slides that will impress your audience, all with Python! Whether you are a (…)
Tesla AI Day Highlights & How Autopilot Works
Self-driving Cars Andrej Karpathy’s talk on Tesla’s autopilot explained clearly in under 10 minutes Originally published on louisbouchard.ai , read it 2 days before on my blog ! Listen to this story… https://medium.com/media/1b73cb1075da96b1c946b197eab6c5c2/href If you wonder how a Tesla car can not only see but navigate the roads with other vehicles, this is the article you were waiting for. A (…)
PyQt & Relational Databases
Easy to use full-featured widget for working with relational database data. Python is an easy-to-learn and powerful programming language. You can get sophisticated outputs, and compared to other languages, you’ll need to write significantly fewer lines of code. However, when it comes to GUI application development, you can experience difficulties. Especially when you need to work with huge (…)
Practical Transfer Learning with PyTorch
Capitalizing on an already functional deep neural network is a huge speedup when solving an ML problem In a previous post, I explained how PyTorch and XGBoost can be combined to perform transfer learning. Transfer learning with XGBoost and PyTorch: hack Alexnet for MNIST dataset This was quite an unconventional way of transferring learning, mixing deep neural networks and gradient Booted trees. (…)
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