Are you looking to continue your learning of natural language processing? This small collection of 3 free top notch courses will allow you to do just that.
The role of the Data Scientist continues to offer many great opportunities as a career. However, the 'sexiest job of the 21st century' has lost some of its appeal because of unrealized expectations and how organizations might leverage this type of work. Having a better understanding of how data science typically plays out in the business world can help you achieve the success you want.
Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.
So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.
Whether you are an aspiring or seasoned Data Scientist, establishing a clear and well-designed online portfolio presence will help you stand out in the industry, and provide potential employers a powerful understanding of your work and capabilities. These tips will help you brainstorm and launch your first data science portfolio.
Understanding your data first is a key step before going too far into any data science project. But, you can't fully understand your data until you know the right questions to ask of it.
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification models.
In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.
So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
The Kedro team and Astronomer have released Kedro-Airflow 0.4.0 to help you develop modular, maintainable & reproducible code with orchestration superpowers!
Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.
One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and how to use it here.
CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.
The foundations of Data Science and machine learning algorithms are in mathematics and statistics. To be the best Data Scientists you can be, your skills in statistical understanding should be well-established. The more you appreciate statistics, the better you will understand how machine learning performs its apparent magic.
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
The Modin library has the ability to scale your pandas workflows by changing one line of code and integration with the Python ecosystem and Ray clusters. This tutorial goes over how to get started with Modin and how it can speed up your pandas workflows.
Out of all the NoSQL database types, document-stores are considered the most sophisticated ones. They store data in a JSON format which as opposed to a classic rows and columns structure.
If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.
PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.
The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.
Writing Python code that works for your data science project and performs the task you expect is one thing. Ensuring your code is readable by others (including your future self), reproducible, and efficient are entirely different challenges that can be addressed by minimizing common bad practices in your development.
Facebook recently introduced and open-sourced their new framework for self-supervised learning of representations from raw audio data called Wav2Vec 2.0. Learn more about it and how to use it here.
Machine learning and data science are founded on important mathematics in statistics and probability. A few interesting mathematical laws you should understand will especially help you perform better as a Data Scientist, including Benford's Law, the Law of Large Numbers, and Zipf's Law.
Have a look at the top 15 YouTube channels for data science by number of subscribers, along with some additional data on the channels to help you decide if they may have some content useful for you.
Top 10 Python Libraries Data Scientists should know in 2021
The Best Machine Learning Frameworks & Extensions for Scikit-learn
Know your data much faster with the new Sweetviz Python library
Top YouTube Channels for Data Science
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