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Beginner’s Guide to AutoML with an Easy AutoGluon Example
This article was published as a part of the Data Science Blogathon Machine Learning is popular and is being used everywhere for applications ranging from financial services to healthcare, marketing & advertising to manufacturing. Almost all industries seem to derive substantial benefit using some form of Machine Learning. Over the recent past, automation technology also […] The post (…)
Visualising Similarity Clusters with Interactive Graphs
Take advantage of Python, Plotly, and NetworkX to create interactive graphs to find similarity clusters Let us assume, as a running example, that my data is composed of word embeddings of the English language. I want to gain insights about the word distribution in the embedding space, specifically, if there are any clusters of very similar words, if there are words that are completely different (…)
Dynamic Mode Decomposition for Spatiotemporal Traffic Speed Time Series in Seattle Freeway
Spatiotemporal traffic data analysis is an emerging area in intelligent transportation systems. In the past few years, data-driven machine learning models have provided new dimensions for understanding real-world data, building data computing paradigm, and supporting real-world applications. In this blog post, we plan to: introduce a publicly available traffic flow data in Seattle, USA, design an (…)
An Introduction To Decision Trees and Predictive Analytics
How can you ensure that a product launch will be successful? Decision trees are a great introduction to using data science for these types of business problems Image by fietzfotos on Pixabay Decision trees represent a connecting series of tests that branch off further and further down until a specific path matches a class or label. They’re kind of like a flowing chart of coin flips, if/else (…)
Intro to Data Structures. Optimize your code and demolish FAANG…
Hands-on Tutorials Optimize your code and demolish FAANG interviews This post took on the humble task of introducing data structures, the ways that programming languages store data. We started by distinguishing the task (abstract data type) from the implementation (data structure), using Big O notation to quantify the efficiency of operations on data structures, and finally the types of data we (…)
Build Your First Mood-Based Music Recommendation System in Python
Hands-on Tutorials Audio-Based Recommendations From Scratch Using the Spotify API Photo by Alena Darmel While music genre plays an enormous role in building and displaying social identity, the emotional expression of a song and — even more importantly — its emotional impression on the listener is often underestimated in the domain of music preferences. Genre is not Enough Only a few decades back, (…)
Automatic Update of Django Models from a Google Spreadsheet
Often Data Model definition and Data Model Implementation are performed by different people and a little change in Data Model definition should be transformed into an implemented model as soon as possible. The speed with which changes in the model can be translated into implementation depends on how definition and implementation are related to each other. In this tutorial I propose a mechanism to (…)
Text Similarity using K-Shingling, Minhashing and LSH(Locality Sensitive Hashing)
Natural Language Processing Text Similarity using K-Shingling, Minhashing, and LSH(Locality Sensitive Hashing) Text similarity plays an important role in Natural Language Processing (NLP) and there are several areas where this has been utilized extensively. Some of the applications include Information retrieval, text categorization, topic detection, machine translation, text summarization, (…)
How Netflix Metaflow helped us build real-world Machine Learning services
This article lives in: Dev.to Medium Towards Data Science Intro: I joined future demand 2 years ago as a software engineer, and during the last year my team and I had been working with Metaflow and in this article, I want to summarize our experience using it. The framework was developed originally at Netflix and it has an active community, and as the framework is still relatively new, I decided (…)
Introduction to Partitioned hive table and PySpark
This article was published as a part of the Data Science Blogathon What is the need for Hive? The official description of Hive is- ‘Apache Hive data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and […] The post Introduction to Partitioned hive table and PySpark appeared (…)
Exploring Data Classes in Python
Get to know data classes
13 Python Advanced Code Snippets for Everyday Problems
An article on using advanced codes to make everyday Python problems easier.
Data Scientist vs. Artificial Intelligence Engineer: Which Is a Better Career Choice?
IDC reported the global spending on AI technologies will hit $97.9 billion by the end of 2023. According to Gartner, 80% of merging technologies will have foundations in AI by the end of 2021. According to LinkedIn’s 2020 Emerging Jobs report, artificial intelligence engineers and data scientists continue to make a strong showing as the top […] The post Data Scientist vs. Artificial Intelligence (…)
How to authenticate Python to access Google Sheets with Service Account JSON credentials
When you need to access Google Sheets data from Python script you will need to prove that you have access to the resource. There are several options to authenticate to Google API, one of them is Service Account. We will show you how to get JSON file with credentials to access Google Sheets.
A Beginner’s Guide to Discrete Time Markov Chains
Closing price of Acme corporation simulated using a 2-state Markov process (Image by Author) And a tutorial on how to simulate a discrete Markov process using Python A Discrete Time Markov Chain can be used to describe the behavior of a system that jumps from one state to another state with a certain probability, and this probability of transition to the next state depends only on what state the (…)
A Comprehensive Guide on Feature Engineering
This article was published as a part of the Data Science Blogathon Why should we use Feature Engineering? Feature Engineering is one of the beautiful arts which helps you to represent data in the most insightful possible way. It entails a skilled combination of subject knowledge, intuition, and fundamental mathematical skills. You are effectively transforming […] The post A Comprehensive Guide on (…)
Pivot Tables in Python With Pandas
A complete guide to one of the most powerful data analytics tools
Intro to Webhooks with Python
This tutorial will be an introduction to the concept of webhooks. We will also build a simple Flask server that can receive GitHub webhooks. We will also see how to expose our local hosts. What is a webhook? Before talking about webhooks, let’s talk about APIs. Below is the data flow for an API. You make a GET/POST request to the API and you get a response back. If you want to learn more about (…)
How Powerful is the Musk Effect?
An article on finding how Elon Musk’s tweets affect the cryptocurrency markets. Continue reading on Python in Plain English »
How to Build an Impressive Data Analytics Portfolio
You’ve decided to get build a career in the data analytics field. But much before you can begin your exciting journey on honing your data analytics skills and push yourself to accomplish things you never thought would be possible, you’ve got to understand an important first step in the career transition process: building a data […] The post How to Build an Impressive Data Analytics Portfolio (…)
Solution of a Regression Problem with Machine Learning in Python using Sklearn and XGBoost and…
Solution of a Regression Problem with Machine Learning in Python using Sklearn and XGBoost and PySpark Machine Learning is commonly used to solve regression problems. More specifically, the application of a regression algorithm to a multi-dimensional dataframe is a method commonly used to measure the degree at which one (or more than on) independent variable (predictors) and more than one (…)
Predicting Strava Kudos
An end-to-end data science project, from data collection to model deployment, aimed at predicting user interaction on Strava activities based on the given activity’s attributes. Strava is a service for tracking human exercise which incorporates social network type features. It is mostly used for cycling and running, with an emphasis on using GPS data. A typical Strava post from myself is shown (…)
Stop Using Deep Learning
Opinion Here’s why and when… , (2021) Stop Using Deep Learning was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Named Entity Recognition with spaCy and NLTK
Figure 1 The initial step towards information extraction is Named Entity Recognition (NER), which aims to locate and classify named entities in text into pre-defined categories such as names of people, organizations, locations, expressions of time, quantities, monetary values, percentages, and so on. NER is utilized in a variety of domains in Natural Language Processing (NLP), and it may assist (…)
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