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Mastering Clustering with a Segmentation Problem - Aug 3, 2021.
The one stop shop for implementing the most widely used models in Python for unsupervised clustering.
30 Most Asked Machine Learning Questions Answered - Aug 3, 2021.
There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.
GPU-Powered Data Science (NOT Deep Learning) with RAPIDS - Aug 2, 2021.
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.
3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks - Aug 2, 2021.
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 - Aug 2, 2021.
Typically, development and testing ETL pipelines is done on real environment/clusters which is time consuming to setup & requires maintenance. This article focuses on the development and testing of ETL pipelines locally with the help of Docker & LocalStack. The solution gives flexibility to test in a local environment without setting up any services on the cloud.
- Data Monetization 101
- 10 Machine Learning Model Training Mistakes
GitHub Copilot Open Source Alternatives
, by Matthew Mayo GitHub's Copilot code generation tool is currently only available via approved request. Here are 4 Copilot alternatives that you can use in your programming today.
- MLOps Best Practices
- A Brief Introduction to the Concept of Data
- dbt for Data Transformation – Hands-on Tutorial
- Building Machine Learning Pipelines using Snowflake and Dask
- Python Data Structures Compared
- Machine Learning Skills – Update Yours This Summer
- Facebook Open Sources a Chatbot That Can Discuss Any Topic
Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics
, by Kevin Vu Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?
- Top Python Data Science Interview Questions
- Full cross-validation and generating learning curves for time-series models
- How to Use Kafka Connect to Create an Open Source Data Pipeline for Processing Real-Time Data
- Overview of Albumentations: Open-source library for advanced image augmentations
- The Lost Art of Decile Analysis
- ColabCode: Deploying Machine Learning Models From Google Colab
- The Best SOTA NLP Course is Free!
- WHT: A Simpler Version of the fast Fourier Transform (FFT) you should know
- When to Retrain an Machine Learning Model? Run these 5 checks to decide on the schedule
11 Important Probability Distributions Explained
, by Terence Shin There are many distribution functions considered in statistics and machine learning, which can seem daunting to understand at first. Many are actually closely related, and with these intuitive explanations of the most important probability distributions, you can begin to appreciate the observations of data these distributions communicate.
- Understanding BERT with Hugging Face
- How Much Memory is your Machine Learning Code Consuming?
Advice for Learning Data Science from Google’s Director of Research
, by Benjamin Obi Tayo Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.
- How to Create Unbiased Machine Learning Models
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5
- Pushing No-Code Machine Learning to the Edge
- 7 Open Source Libraries for Deep Learning Graphs
Top 6 Data Science Online Courses in 2021
, by Natassha Selvaraj As an aspiring data scientist, it is easy to get overwhelmed by the abundance of resources available on the Internet. With these 6 online courses, you can develop yourself from a novice to experienced in less than a year, and prepare you with the skills necessary to land a job in data science.
- Date Processing and Feature Engineering in Python
- Shareable data analyses using templates
Geometric foundations of Deep Learning
, by Michael Bronstein, Joan Bruna, Taco Cohen, and PV Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.
- SQL, Syllogisms, and Explanations
- Streamlit Tips, Tricks, and Hacks for Data Scientists
Become an Analytics Engineer in 90 Days
, by Tuan Nguyen A new role of the Analytics Engineer is an exciting opportunity that crosses the skill sets of a Data Analyst and Data Engineer. Here, we describe how this position can evolve at an organization, and recommend self-learning resources that can be used to prepare for the multifaceted responsibilities.
- How to Tell if You Have Trained Your Model with Enough Data
- Exploring the SwAV Method
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4
- 5 Python Data Processing Tips & Code Snippets
- A Lightning Fast Look at Single Line Exploratory Data Analysis
Pandas not enough? Here are a few good alternatives to processing larger and faster data in Python
, by DaurEd While the Pandas library remains a crucial workhorse in data processing and management for data science, some limitations exist that can impact efficiencies, especially with very large data sets. Here, a few interesting alternatives to Pandas are introduced to improve your large data handling performance.
- MLOps is an Engineering Discipline: A Beginner’s Overview
- How to Get Practical Data Science Experience to be Career-Ready
- How to Build An Image Classifier in Few Lines of Code with Flash
- ROC Curve Explained
- A Learning Path To Becoming a Data Scientist
- GitHub Copilot: Your AI pair programmer – what is all the fuss about?
- Predict Customer Churn (the right way) using PyCaret
- Semantic Search: Measuring Meaning From Jaccard to Bert
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3
- Prepare Behavioral Questions for Data Science Interviews
- How to Use NVIDIA GPU Accelerated Libraries
- Learning Data Science Through Social Media