Artificial Intelligence Feed’s Post

A Step-by-Step Guide to Build a Graph Learning System for a Movie Recommender Built with PyTorch Geometric and using MovieLens DataSetContinue reading on Towards Data Science »... https://lnkd.in/eDnmtZK6 #AI #ML #Automation

I just completed a project which involved analyzing the speeches of Imran Khan, the former Prime Minister of Pakistan, through a data science lens. Check out the Kaggle Notebook for a deeper dive into the methodology and results https://lnkd.in/d3d9yBb7. The key objectives of this project were: 1. Preprocessing textual data for NLP tasks. 2. Building topic models to identify latent themes in the speeches. 3. Sentiment analysis to assess the emotional tone. 4. Exploring lexical diversity to study vocabulary richness. 5. Visualizations to present findings effectively. 📊 Results: 1. Topic Modeling revealed 5 significant themes, illustrated with engaging visualizations. 2. Sentiment Analysis indicated a slightly positive tone in the speeches, peaking in 2021. 3. Lexical Diversity analysis highlighted variations in vocabulary richness over time. The code is also available on GitHub and can be accessed through this link: https://lnkd.in/d_3H_7yj

Today's focus was all about mastering NumPy arrays! Here’s a breakdown of what I covered: 1. Creating Reproducible Arrays: I learned how to create a one-dimensional array of random integers between 0 and 20, ensuring reproducibility by setting the seed. 2. Slicing Arrays: I practiced slicing by creating a subset from an array using specific indices from my original array. 3. Working with Two-Dimensional Arrays: I transformed a list into a 2D array and sliced it. Seeing how NumPy's power allows for efficient data manipulation is incredibly rewarding. Each day I feel one step closer to mastering data science! thank you OLUOKUN ADEWUMI ESTHER #desire #ML #AI

"Exploring the world of data processing tools! 🚀 This captivating image showcases operations in Pandas, Polars, SQL, and PySpark, offering a glimpse into the diverse landscape of data manipulation. From the intuitive interface of Pandas to the blazing fast performance of Polars, and the structured querying power of SQL to the distributed computing prowess of PySpark, each tool brings its own strengths to the table. As data enthusiasts, it's essential to navigate these options wisely to unlock insights efficiently and effectively. Which tool do you find most compelling for your data projects? Let's spark a conversation! #AI #Innovation #MachineLearning #DataAnalytic

🗣 Upcoming How-To Session! 📑 Building Knowledge Graphs from Unstructured Data 🗓 August 14th at 09:00 PT / 18:00 CEST Don't miss our upcoming session where we dive into the techniques for transforming unstructured data into knowledge graphs! Register now 👉 https://lnkd.in/gvKxz7Gx Our session will be led by Paco Nathan, an experienced expert known for his insightful tutorials. In this workshop, Paco will walk you through the broader practices of knowledge graph creation, from parsing text using tools like spaCy to constructing lexical graphs with textgraph methods. You’ll discover how to overlay a semantic layer using named entity recognition, entity extraction, and entity linking. By employing relation extraction, we'll connect nodes and enhance graph semantics. Throughout, we'll be leveraging LLMs and deep learning models for task-specific improvements. Using domain-specific resources, such as a thesaurus, we’ll show you how to perform semantic random walks, expanding the graph further. Finally, we'll explore graph analytics for practical applications, including GraphRAG. Join us and enhance your understanding and skills in knowledge graph construction! #KnowledgeGraph #UnstructuredData #DataScience #MachineLearning #TechWorkshops

A Visual Understanding of Decision Trees and Gradient Boosting A visual explanation of the math behind decision trees and gradient boostingContinue reading on Towards Data Science »... https://lnkd.in/ex2NZt9p #AI #ML #Automation

Just finished the course “AI Fundamentals for Data Professionals”

If you haven’t experienced Paco’s teaching then you should. He’s great at explaining complex problems and at pointing out how they can be applicable to a particular domain.

PyTorch Optimizers Arent Fast Enough. Try These Instead These 4 advanced optimizers will open your mind.Continue reading on Towards Data Science »... https://lnkd.in/e8vbEjNq #AI #ML #Automation

🚀 New to #DataScience? Wondering why #LinearAlgebra and #NumPy are crucial tools? This lecture is for you! Video: https://lnkd.in/drYW9bRC 📊 In this comprehensive lecture, we demystify the relationship between Linear Algebra and NumPy and show you exactly why these concepts are foundational in AI and Data Science. 💡 We seamlessly integrate theory and practice, providing clear explanations and real-world applications. You’ll learn how key concepts like vectors, matrices, dot products, normalization, and cosine similarity play a vital role in AI and Data Science tasks such as semantic search, word embeddings, and computer vision—all while using NumPy for hands-on implementation. 💻 Whether you’re just starting your data science journey or looking to solidify your understanding, this course offers tangible, practical use cases that will help you see the bigger picture of how Linear Algebra and NumPy power data science applications. 🔗 Watch the lecture now and discover how these essential tools can elevate your data science skills! #DataScience #AI #LinearAlgebra #NumPy #MachineLearning #SemanticSearch #WordEmbeddings