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What are the different types of data structures? Here's a refresher for all machine learning beginners! ⬇ Some common types of data structures are: 🔵 Arrays: Arrays are a collection of elements stored at contiguous memory locations. They are useful for storing a fixed-size sequential collection of elements of the same type. 🔵 Linked Lists: Linked lists are data structures consisting of a sequence of elements, where each element points to the next one. They can be singly linked, doubly linked, or circular. 🔵 Stacks: Stacks are abstract data types that follow the Last In, First Out (LIFO) principle. Elements are added and removed from the same end, called the top of the stack. 🔵 Queues: Queues are abstract data types that follow the First In, First Out (FIFO) principle. Elements are added at the rear end and removed from the front end. 🔵 Trees: Trees are hierarchical data structures consisting of nodes connected by edges. They have a root node, internal nodes, and leaf nodes. Common types of trees include binary trees, binary search trees, AVL trees, red-black trees, etc. 🔵 Graphs: Graphs are non-linear data structures consisting of nodes and edges that connect these nodes. They can be directed or undirected, weighted or unweighted. 🔵 Hash Tables: Hash tables are data structures that store key-value pairs. They use a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. 🔵 Heaps: Heaps are specialized tree-based data structures that satisfy the heap property. Common types include binary heaps and Fibonacci heaps, which are often used for priority queue implementations. 🔵 Trie: Trie, also known as a prefix tree, is a tree-like data structure used for storing a dynamic set of strings. It's particularly efficient for string-related operations like prefix matching. 🔵 Graph Representations: Besides the abstract concept of a graph, there are different ways to represent graphs in memory, including adjacency matrix, adjacency list, and edge list. Choosing the appropriate data structure is crucial for designing efficient algorithms and applications. Which one do you prefer the most? #ai #machinelearning #datascience #data #dataarchitecture #dataanalytics #aiapplications #aiapps #artificialintelligence #generativeai #genai

We all know the opportunities that LLMs bring, but how can we put it into practice? Julian Wiffen, Director of Data Science for the Product team at Matillion, will guide you through AI prompt engineering in his three-part blog series.💡 Part 1 https://okt.to/wZj92U Part 2 https://okt.to/EdTvuj Part 3 https://okt.to/Q6RmK7

📚🤓 Excited to share some insights about K-Nearest Neighbors (KNN) in #DataScience! 📊 KNN Classification and Regression 🧐📈 KNN is a fundamental algorithm in data science that's all about neighbors! 🏡🏡 It's like asking your neighbors for advice when you need it the most. Let's dive into this simple yet powerful concept. 🔍 What is KNN? KNN is a supervised learning algorithm used for both classification and regression tasks. It helps us make predictions based on the similarity of data points. 📊✅ How Does it Work? Imagine you want to classify a point on a graph. KNN checks the labels of the 'K' nearest data points (neighbors) to the point you're interested in. Majority wins! 🗳️👥 Key Concepts: K Value: You choose the 'K' value, which is the number of neighbors to consider. A small 'K' can be sensitive to noise, while a large 'K' can be too general. Distance Metrics: Common distance metrics include Euclidean and Manhattan. They determine how "close" or "similar" points are. Classification vs. Regression: Classification: KNN assigns a class label based on the majority class of its neighbors. Regression: KNN calculates the average or weighted average of the neighbors' values to make a prediction. Pros: Simple to implement and understand. No assumptions about data distribution. Versatile for various types of data. Cons: Sensitive to noisy data. Computationally expensive for large datasets. Use Cases: Recommender Systems 🎬📚 Image and Handwriting Recognition 📷✍️ Medical Diagnosis 🏥💉 Stock Price Prediction 📈💰 Remember: Choosing the right 'K' and distance metric is crucial. Preprocessing and feature scaling can improve KNN's performance. KNN may not perform well with high-dimensional data. Ready to try KNN in your data science projects? Dive in, experiment, and discover its magic! 🔮✨ #KNN #MachineLearning #DataAnalysis #DataScience #Classification #Regression #LearningDataScience #DataScienceJourney #AI #DataScientists #PredictiveModeling Krish Naik sudhanshu kumar Let's keep the data science community buzzing with knowledge! 🚀🧠 Share your thoughts and experiences with KNN in the comments below. 👇📝

Show your customers how to unlock data potential with Cloudera #OpenDataLakehouse powered by The Apache Software Foundation #Iceberg ✔️Beak silos ✔️Centralize security ✔️Accelerate AI, BI, and machine learning projects #ClouderaPartners #data #realtimeanalytics

This is a very interesting approach and awareness of alternative to scenarios where vector search and RAG are simply insufficient. A good tool to keep in mind for Gen AI data architectures #generativeai #dataarchitecture

Hello to All My LinkedIn community, I'm excited to invite you to dive into my newest blog post titled "Navigating the Data Universe: Advanced Strategies for AI-Driven Analytics". This piece is a deep dive into leveraging BigQuery for data science, packed with actionable insights and innovative strategies to unleash its full power. 🔗 [Explore "Navigating the Data Universe: Advanced Strategies for AI-Driven Analytics"](https://lnkd.in/eGJSCDwZ) I highly value your thoughts and engagement as we delve into the intricate world of data ecosystems together. This article is a gateway to understanding the synergies between Data Science, MLOps, and the forefront of AI innovations. Let’s start a meaningful conversation that bridges disciplines, encouraging shared insights and collective advancements. Join me in this journey to harness the extraordinary capabilities of data, setting the stage for groundbreaking developments. #AdvancedDataScience #InnovativeMLOps #AIBreakthroughs #BigQueryExpertise #DataScienceEvolution #FutureOfAI #TechInsightsReimagined #JillaniTech #DigitalTransformation #AIExplorations #TrendingTech #MLOpsCulture #NotebookInnovations #AIForGood #DataEcosystems #CloudComputing #BigDataSolutions #TechCommunityEngagement #GoogleCloudPlatform #DataAnalyticsTrends #ai #machinelearning #datascience Excited to see your perspectives and connect more deeply with each of you.

This still applies! The backend is much more complicated. Abstracting the complexity doesn’t mean trivializing what’s under the hood. �� Subscribe to my Enterprise Data Science newsletter to learn more about this complexity, and how to deliver it for large enterprises. 🔗 to my newsletter in comments 👇🏽 Credit: Data Science Central, circa 2022 #ai #artificialintelligence #enterprise #datascience

Have you missed the Data+AI Summit ? Don't worry we got you covered. Here is the Gen AI DAIS recap presented by my amazing colleagues Lara Rachidi and Maria Zervou. Next newsletter will be about Data Engineering recap 😎 #Databricks #DataAISummit

The quality of your data determines the quality of your assessment. One of the biggest challenges I faced during my project on food waste was finding key data for my questions, and it limited the scope of my research. Although I gained access to some useful databases, it was challenging interpreting the coding of the data due to language barrier. Thanks to Himanshu Ramchandani, he's made a collection of data repository which can be useful for your research/project. Be sure to check it out. This will save you time. #dataanalysis #datacollection #datacommunity #dataanalyst #datavisualization

Are you at Databricks Data + AI Summit this week? I am excited about the sessions and interactions with industry leaders. A special shoutout to Prophecy for their incredible lineup of events and sessions: https://lnkd.in/dFUHdMGE Particularly, I am looking forward to attending this one! -- Designing a Copilot for Data Transformation with Spark and SQL AI is accelerating every aspect of data management. Hear from Prophecy co-founders Raj Bains and Maciej Szpakowski on creating an AI Copilot to make all data users 10x more productive. Wednesday, June 12, 2024
 Moscone South, Esplanade, Room 151 11:20 AM - 12:00 PM P.S. Meet the Authors: Don’t miss the chance to meet the authors behind some of the most influential books in the data engineering field. Gain insights straight from the minds who are shaping the future of data. Looking forward to meeting my friends, Sol Rashidi, Matthew Housley, and Adi Polak! #data #ai #dataaisummit #databricks #theravitshow