PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient. PyCaret is simple and easy to use.

We have a serious problem with how we have been teaching computability theory, a central component of the ACM/IEEE computer science curriculum. For a fair number of years, I taught a computability course.

This interactive workshop introduces the principles and practices of machine learning using the Python programming language and its associated …

Description Unsupervised Machine Learning involves finding patterns in datasets. After taking this course, students will be able to understand, implement in Python, and apply algorithms of Unsupervised Machine Learning to real-world datasets. The core of this course involves detailed study of the following algorithms: Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model Dimension Reduction: Principal Component Analysis The course presents the math underlying these algorithms including normal distributions, expectation maximization, and singular value decomposition. The course also presents detailed explanation of code design and implementation in Python, including use of vectorization for speed up, and metrics for measuring quality of clustering and dimension reduction. The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).

Prifina is thrilled to welcome Dr. Peter Cotton as our special guest in the fifth episode of the "Liberty. Peter currently serves as the Senior Vice President and Chief Data Scientist at Intech Investment Management LLC. D. degree in Mathematics from Stanford, he held leadership roles at major U.S. financial institutions. Peter has led data science projects at Morgan Stanley, J.P. Morgan Chase, and several major hedge funds, where he built solutions solving complex data problems. He has extensive experience with crowdsourcing models and helped build one of the first in the world privacy-preserving computation mechanisms at J.P. Morgan Chase. In this podcast we talk about algorithms and innovation, with a focus on financial data. How does a hedge fund normalize messy financial data to build bespoke predictive models? What are the current trends and challenges related to machine learning in the financial services industry? From the innovation point of view, what happens when we cut down the cost of building algorithms to minimum functionality? Is it possible to build personal AI systems for small and mid-cap companies as well as individuals? We also delve into geeky topics, such as how to build a financial probability model for the pricing of vanilla bonds. You can find this podcast on Spotify, Apple Podcasts, Google Podcasts, and SoundCloud. He noted that one of the main areas of focus in his career was to level the playing field in the machine learning (ML) space. He notes that one of the first things to do before building financial prediction, ML, and AI models is create a system that helps clean and normalize data. "It is the most interesting mathematical problem of all because the cleaning of data implies that you understand the market itself.

There is certainly an important role AI and Machine Learning technology plays in addressing the most sophisticated online fraud as well as improving …

Build 5 Practical Projects & Harness the Power of AI to solve practical, real-world business problems with Zero Coding! Do you want to build super powerful applications in Artificial intelligence (AI) but you don't know how to code? Are you intimidated by AI and don't know where to start? Or maybe you don't have a computer science degree and want to break into AI? Are you an aspiring entrepreneur who wants to maximize business revenue and reduce costs with AI but don't know how to get there quickly and efficiently?

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In the series, we considered both the teaching of AI and machine learning itself, and the use of AI techniques to improve education in general. You can also find out more about conferences and events, and other interesting research at the intersection of AI and education. There are a number of conferences and workshops that focus on the education side of AI. In our focus series we heard from the co-chairs of the Symposium on Educational Advances in Artificial Intelligence (EAAI), which was held in February this year. This event is held as an independent symposium within the AAAI conference, and provides the opportunity for researchers, educators, and students to share educational experiences involving AI.

During the progress of any data science project, most data scientists tend to utilize tools and gadgets that would help them reach their goals faster and more efficiently. They use these tools to speed up routine tasks to save their energy and brain-power to find solutions for the current problem they are trying to solve. Because of this desire to speed up a project's workflow, there are so many data science tools out there that you can choose from, whichever suits the task at hand. And believe me, when I say this, there are hundreds of tools you can choose to finish your project; at the end of the project, you will discover that you used multiple of these tools to finish one project. Since any data science project consists of different steps, from gathering and collecting data to clean it, analyzing, and visualizing it, there are tools designed and developed for each of these steps. Tools to automatically collect data for you from all over the web, or tools to visualize your data and help you tell the story hidden within, or tools to help you clean your data and use the most relevant part of it in your analysis.