Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.
This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more.
It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.
Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies.
Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!
A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.
We take a hard look at diversity within the tech industry, root causes, and potential solutions and highlight resources/initiatives that can connect readers with programs aiding their professional development.
An overview of how an information extraction pipeline built from scratch on top of deep learning inspired by computer vision can shakeup the established field of OCR and data capture.
Detailed analysis into utilizing deep learning on the edge, covering both advantages and disadvantages and comparing this against more traditional cloud computing methods.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
In this tutorial, you will learn how to create a table, insert values into it, use and understand some data types, use SELECT statements, UPDATE records, use some aggregate functions, and more.
We examine the famous McKinsey prediction from 2011 and look into whether there a shortage of people with analytical expertise and estimate how many Data Scientists are there.
An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.
Cognitive biases are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment. They have all sorts of practical impacts on our lives, whether we want to admit it or not.
We still have a long way to go before the gender representation becomes more equalized, but the field at large indicates hopeful trends about women working in the role or desiring to do so in the future.
In this article, focus on current AI, which is mostly based on the algorithms that can do predictions, and discuss how the economics of AI works and how it may affect business.
Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Introducing Octoparse - a sleek, powerful and easy-to-use software that makes web scraping from any websites achievable for most people, including non-coders.
Watch Springboard webinar and learn everything from the hard skills to the soft skills aspiring data scientists need. Springboard Data Science Career Track now offers deferred tuition - learn more.
This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
This article presents 5 things to know about A/B testing, from appropriate sample sizes, to statistical confidence, to A/B testing usefulness, and more.
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.
If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.
A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.
An overview and discussion around data science, covering the history behind the term, data mining, statistical inference, machine learning, data engineering and more.
In this first part I show how to clean and remove unnecessary features. Data processing is very time-consuming, but better data would produce a better model.
Based on the recent book - Principles of Database Management - The Practical Guide to Storing, Managing and Analyzing Big and Small Data - this post examines how OLAP queries can be implemented in SQL.
Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.
Also: AI Knowledge Map: How To Classify AI Technologies; How to Make Your Machine Learning Models Robust to Outliers; Linear Regression In Real Life; 5 Data Science Projects That Will Get You Hired in 2018