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Latest:
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Paradoxes in Data Science - Sep 17, 2021.
Have a look into some of the main paradoxes associate with Data Science and it’s statistical foundations. -
Introducing TensorFlow Similarity - Sep 17, 2021.
TensorFlow Similarity is a newly-released library from Google that facilitates the training, indexing and querying of similarity models. Check out more here. -
Adventures in MLOps with Github Actions, Iterative.ai, Label Studio and NBDEV - Sep 16, 2021.
This article documents the authors' experience building their custom MLOps approach. -
The Machine & Deep Learning Compendium Open Book - Sep 16, 2021.
After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management. -
Easy SQL in Native Python - Sep 16, 2021.
If the idea of being able to link with SQL databases and define, manipulate, and query using Python sounds appealing, check out the SQLModel library. -
Introduction to Automated Machine Learning - Sep 15, 2021.
AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications. -
How to get Python PCAP Certification: Roadmap, Resources, Tips For Success, Based On My Experience - Sep 15, 2021.
Follow this journey of personal experience -- with useful tips and learning resources -- to help you achieve the PCAP Certification, one of the most reputed Python Certifications, to validate your knowledge against International Standards. -
5 Must Try Awesome Python Data Visualization Libraries - Sep 15, 2021.
The goal of data visualization is to communicate data or information clearly and effectively to readers. Here are 5 must try awesome Python libraries for helping you do so, with overviews and links to quick start guides for each. -
Speeding up Neural Network Training With Multiple GPUs and Dask - Sep 14, 2021.
A common moment when training a neural network is when you realize the model isn’t training quickly enough on a CPU and you need to switch to using a GPU. It turns out multi-GPU model training across multiple machines is pretty easy with Dask. This blog post is about my first experiment in using multiple GPUs with Dask and the results. -
Data Scientists Without Data Engineering Skills Will Face the Harsh Truth - Sep 14, 2021.
Although the role of the data scientist is still evolving, data remains at its core. Setting the right expectations for what you will do as a data scientist is important, and, to be sure, knowing the tools of data engineering will get yourself ready for the real world. -
An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab - Sep 14, 2021.
Get an Introduction to Reinforcement Learning by attempting to balance a virtual CartPole with OpenAI Gym, RLlib, and Google Colab. -
The Prefect Way to Automate & Orchestrate Data Pipelines - Sep 13, 2021.
I am migrating all my ETL work from Airflow to this super-cool framework. -
3 Most Important Lessons I’ve Learned 3 Years Into My Data Science Career - Sep 13, 2021.
After only 3 years of working as a data professional, many tried-and-true lessons can be learned. Here are 3 of the most important lessons learned with key takeaways and reflections shared. -
Working with Python APIs For Data Science Project - Sep 10, 2021.
In this article, we will work with YouTube Python API to collect video statistics from our channel using the requests python library to make an API call and save it as a Pandas DataFrame. -
A Data Science Portfolio That Will Land You The Job - Sep 10, 2021.
Landing a data science job is no easy feat, especially during the COVID-19 pandemic. This article provides aspiring data scientists with advice on building a data science portfolio that stands out. -
Text Preprocessing Methods for Deep Learning - Sep 10, 2021.
While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too. -
How to Create an AutoML Pipeline Optimization Sandbox - Sep 9, 2021.
In this article, we will implement an automated machine learning pipeline optimization sandbox web app using Streamlit and TPOT. -
8 Deep Learning Project Ideas for Beginners - Sep 9, 2021.
Have you studied Deep Learning techniques, but never worked on a useful project? Here, we highlight eight deep learning project ideas for beginners that will help you sharpen your skills and boost your resume. -
7 Differences Between a Data Analyst and a Data Scientist - Sep 9, 2021.
This article discusses the 7 key differences between data analysts and data scientists with an aim to help potential data analysts/scientists determine which is the right one for them. I touch on day-to-day tasks, skill requirements, typical career progression, and salary and career prospects for both. -
Top 18 Low-Code and No-Code Machine Learning Platforms - Sep 8, 2021.
Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI. -
How Machine Learning Leverages Linear Algebra to Solve Data Problems - Sep 7, 2021.
Why you should learn the fundamentals of linear algebra. -
ebook: Learn Data Science with R – free download - Sep 7, 2021.
Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content. -
How to Create Stunning Web Apps for your Data Science Projects - Sep 7, 2021.
Data scientists do not have to learn HTML, CSS, and JavaScript to build web pages. -
Fast AutoML with FLAML + Ray Tune - Sep 6, 2021.
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster. -
Five Key Facts About Wu Dao 2.0: The Largest Transformer Model Ever Built - Sep 6, 2021.
The record-setting model combines some clever research and engineering methods. -
Hypothesis Testing Explained - Sep 3, 2021.
This brief overview of the concept of Hypothesis Testing covers its classification in parametric and non-parametric tests, and when to use the most popular ones, including means, correlation, and distribution, in the case of one sample and two samples. -
6 Cool Python Libraries That I Came Across Recently - Sep 3, 2021.
Check out these awesome Python libraries for Machine Learning. -
Build a synthetic data pipeline using Gretel and Apache Airflow - Sep 2, 2021.
In this blog post, we build an ETL pipeline that generates synthetic data from a PostgreSQL database using Gretel’s Synthetic Data APIs and Apache Airflow. -
Best Resources to Learn Natural Language Processing in 2021 - Sep 2, 2021.
In this article, the author has listed listed all the best resources to learn natural language processing including Online Courses, Tutorials, Books, and YouTube Videos. -
Do You Read Excel Files with Python? There is a 1000x Faster Way - Sep 1, 2021.
In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude. -
Data Science Cheat Sheet 2.0 - Sep 1, 2021.
Check out this helpful, 5-page data science cheat sheet to assist with your exam reviews, interview prep, and anything in-between. -
How is Machine Learning Beneficial in Mobile App Development? - Sep 1, 2021.
Mobile app developers have a lot to gain by implementing AI & Machine Learning from the revolutionary changes that these disruptive technologies can offer. This is due to AI and ML's potential to strengthen mobile applications, providing for smoother user experiences capable of leveraging powerful features.
August:
- CSV Files for Storage? No Thanks. There’s a Better Option
- Multilabel Document Categorization, step by step example
- A Python Data Processing Script Template
- Introducing Packed BERT for 2x Training Speed-up in Natural Language Processing
- Data Science Project Infrastructure: How To Create It
- 3 Data Acquisition, Annotation, and Augmentation Tools
- 11 Best Data Science Education Platforms
- 15 Python Snippets to Optimize your Data Science Pipeline
- How to Engineer Date Features in Python
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Learning Data Science and Machine Learning: First Steps After The Roadmap
, by Harshit Tyagi Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML. -
Automate Microsoft Excel and Word Using Python
, by Mohammad Khorasani Integrate Excel with Word to generate automated reports seamlessly. - How to Select an Initial Model for your Data Science Problem
- Enhancing Machine Learning Personalization through Variety
- When Correlation is Better than Causation
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Open Source Datasets for Computer Vision
, by Kevin Vu Access to high-quality, noise-free, large-scale datasets is crucial for training complex deep neural network models for computer vision applications. Many open-source datasets are developed for use in image classification, pose estimation, image captioning, autonomous driving, and object segmentation. These datasets must be paired with the appropriate hardware and benchmarking strategies to optimize performance.
- Data Scientist’s Guide to Efficient Coding in Python
- Linear Algebra for Natural Language Processing
- Model Drift in Machine Learning – How To Handle It In Big Data
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Prefect: How to Write and Schedule Your First ETL Pipeline with Python
, by Dario Radečić Workflow management systems made easy — both locally and in the cloud. - Agile Data Labeling: What it is and why you need it
- Writing Your First Distributed Python Application with Ray
- How to Train a BERT Model From Scratch
- Querying the Most Granular Demographics Dataset
- Introduction to Statistical Learning Second Edition
- MLOps And Machine Learning Roadmap
- How to Detect and Overcome Model Drift in MLOps
- DeepMind’s New Super Model: Perceiver IO is a Transformer that can Handle Any Dataset
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Practising SQL without your own database
, by Hui XiangChua SQL is a very important skill for data analysts and data scientists. However, when you are just starting out learning in the field, how can you practice querying with SQL if you don’t have any data stored in a database?
- Visualizing Bias-Variance
- 5 Tips for Writing Clean R Code
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How to Query Your Pandas Dataframe
, by Matthew Przybyla A Data Scientist’s perspective on SQL-like Python functions.
- Using Twitter to Understand Pizza Delivery Apprehension During COVID
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Bootstrap a Modern Data Stack in 5 minutes with Terraform
, by Tuan Nguyen What is a Modern Data Stack and how do you deploy one? This guide will motivate you to start on this journey with setup instructions for Airbyte, BigQuery, dbt, Metabase, and everything else you need using Terraform.
- Essential Math for Data Science: Introduction to Systems of Linear Equations
- Be Wary of Automated Feature Selection — Chi Square Test of Independence Example
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Most Common Data Science Interview Questions and Answers
, by Nate Rosidi After analyzing 900+ data science interview questions from companies over the past few years, the most common data science interview question categories are reviewed in this guide, each explained with an example.
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How To Become A Freelance Data Scientist – 4 Practical Tips
, by Pau Labarta Bajo If you are a nerd-ish data scientist who wants to start working as an independent (remote) freelance data scientist, then these four practical tips can help you transition from the traditional 9-to-5 job to a dynamic experience as a remote contractor, just as the author did three years ago.
- How DeepMind Trains Agents to Play Any Game Without Intervention
- Mastering Clustering with a Segmentation Problem
- 30 Most Asked Machine Learning Questions Answered
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GPU-Powered Data Science (NOT Deep Learning) with RAPIDS
, by Tirthajyoti Sarkar How to utilize the power of your GPU for regular data science and machine learning even if you do not do a lot of deep learning work.
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3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks
, by Terence Shin While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.
- Development & Testing of ETL Pipelines for AWS Locally
