Agencies, such as the U.S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.

Demystify the world of Machine Learning and build core Data Science & predictive analytics skills, without writing code! Created by Maven Analytics, Joshua McCarty 2.5 hours on-demand video course If you're excited to explore data science & machine learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes", "logistic regression", "KNN" and "decision trees", you're in the right place. This course makes data science approachable to everyday people, and is designed to demystify powerful machine learning tools & techniques without trying to teach you a coding language at the same time.

Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online. In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models. Who is this course for? You've given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables.

Learn the approaches and tools to tune hyperparameters and improve the performance of your machine learning models. Created by Soledad Galli 7.5 hours on-demand video course Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models. If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning.

Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like "eye is to seeing what ear is to hearing," sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge. Large language models learn to write humanlike text by internalizing billions of examples from the public web.

Recently, Andrew Ng took to the professional networking platform to announce that the Specialization on Machine Learning Engineering for Production (MLOps) by DeepLearning.AI is now available on Coursera. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. NG posted, "I'm thrilled DeepLearning.AI's Machine Learning Engineering for Production (MLOps) specialization is now available on Coursera!" "Being able to train ML models is essential. And, to build an effective AI career, you need production engineering skills as well, to build and deploy ML systems. With this specialization, you can grow your knowledge of ML into production-ready skills" the AI expert added.

This story originally appeared on Atlas Obscura and is part of the Climate Desk collaboration. If you decide to pursue a degree in geology, be prepared to spend some time in the wilderness, where you will be asked to find and analyze rocks that will help teach you how the planet works. You will sketch curious outcrops, smash stone to pieces, peer at crystals through a hand lens, and, every now and then, even lick rocks, if it comes to that, all under the watchful, judging eye of your instructors. When the pandemic kicked into gear back in March 2020, these both scintillating and stressful field schools were no more. Geology instructors across the world were at a bit of a loss as to what to do.

Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models. No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success. "Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases," said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.

I want to expand this work into a series of µ-tutorial videos. If you're interested, please subscribe to my newsletter to stay in touch. It is not hard to be under an impression that the world is all about neural networks these days when it comes to making models. Many teams seem to brag about super-cool architectures as if getting enough quality data was straightforward, GPU racks were open 24/7 (for free), and their customer's patience was set to infinity. In this article, we will present one of the most basic machine-learning algorithms known as a Decision Tree.

Python is one of the most widely used programming languages in the data science field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you are looking for the Best Books on Data Science with Python, then you should check these books. In this article, you will find 8 Best Books on Data Science with Python. These books will give you in-depth knowledge starting from basics to advanced level.