This article features 19 data science projects for beginners, categorized into 7 full project tutorials, 5 places to come up with your own data science projects using data, and 7 skills-based data science projects.
In this article, we are going to compare the sentiment extraction performance between Sentiment Analysis engines and Custom Text classification engines. The idea is to show pros and cons of these two types of engines on a concrete dataset.
As a result of the efforts outlined in this article, we confirmed that clustering through crowdsourcing is indeed possible and works impressively well.
Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.
PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.
This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next article in the series is about parallelizing for loops, and other embarrassingly parallel operations with dask.delayed.
The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.
We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.
The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.
The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities using the PyTorch C++ API such that the same code can be built for use on mobile platforms (both Android and iOS).
In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. But this format is not optimized for deep learning work. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists.
With a lot of excitement and research around NLP, there are growing opportunities to apply these technologies to real-world scenarios. It's not trivial to become familiar with NLP and these open-source data sets can help you increase your skills.
Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.
If you are beginning your data science journey, then you must be prepared to plan it out as a step-by-step process that will guide you from being a total newbie to getting your first job as a data scientist. These tips and educational resources should be useful for you and add confidence as you take that first big step.
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.
In this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems.
Data that varies in time can offer powerful applications and use cases for data scientists to analyze. This overview considers the top techniques you can learn to understand and gain insight from time-series data.
Most Common SQL Mistakes on Data Science Interviews
How I Redesigned over 100 ETL into ELT Data Pipelines
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