On the previous episodes, we generically spoke about datasets. However, it's time to address a critical concept in data science and machine learning: the curse of dimensionality. This phenomenon surfaces as we increase the number of features in a dataset, leading to more complex and less intuitive data structures. It presents unique challenges in analyzing and interpreting data, emphasizing the need for careful consideration in high-dimensional spaces. #ai #machinelearning #datascience #mathematics #curseofdimensionality #dimensions Sami Heddid Amine Ben Slama Bouthaina Chaabaoui, Eng., MSc.
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What is Multicollinearity? Most experts say this is one of a Data Science interview's most asked technical questions. Multicollinearity is one of the six assumptions of multiple linear regression, making it a simple yet nuanced concept to understand. Simply put, Multicollinearity is a statistical phenomenon that occurs when two or more independent variables in a multiple regression are highly correlated. In other words, these variables exhibit a strong linear relationship, making it difficult to isolate the individual effects of each variable on the dependent variable. Read the detailed article on Medium. https://lnkd.in/gDJ7kep3 #datascience #linearregression #data #machinelearning #ai #ds
Multicollinearity Explained: Dealing with Correlated Variables in Regression Analysis
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Unlock the Power of Machine Learning: Top 10 Must-Know Algorithms for Data Scientists in 2024 Are you a data enthusiast looking to sharpen your skills and stay ahead in the game? Check out my latest article that dives deep into the top 10 must-know machine learning algorithms every data scientist should master. From Linear Regression to Neural Networks, this comprehensive guide covers: 🔹 Essential Algorithms: Learn about the core algorithms transforming data science. 🔹 Practical Examples: Get hands-on insights and real-world applications. 🔹 Best Practices: Discover tips to enhance your projects and drive innovation. In the ever-evolving field of data science, staying updated is crucial. This article is designed to provide you with the knowledge and tools to excel in your career. Read more and elevate your data science journey! https://lnkd.in/gTiVdiJu #MachineLearning #DataScience #ArtificialIntelligence #MLAlgorithms #TechInnovation #DataScientists #AI #DeepLearning #TechTrends #DataAnalytics
Unlock the Power of Machine Learning: Top 10 Must-Know Algorithms for Data Scientists in 2024 - FuturisticGeeks
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Groundbreaking research study in Scientific Reports introduces CSBBoost, a novel approach to tackle one of the biggest challenges in data science: classifying imbalanced data. The study by Amir Reza Salehi & Majid Khedmati presents the cluster-based SMOTE both-sampling (CSBBoost) algorithm, a sophisticated method that combines over-sampling, under-sampling and various ensemble algorithms like XGBoost, random forest and bagging. This approach has been carefully designed to effectively balance datasets while avoiding common pitfalls such as data redundancy after over-sampling, information loss during under-sampling, and randomization in sample selection and generation. The authors tested the algorithm on 20 imbalanced benchmark datasets and a real-world dataset, showcasing its superior performance in terms of precision, recall (F1 score) and the area under the receiver operating characteristics curve (AUC). The CSBBoost algorithm represents a significant advance, offering a more nuanced and effective approach to classifying imbalanced data. Whether you are a data scientist, research enthusiast or simply curious about the latest scientific innovations, this publication sheds light on an important area of data science and promises improved accuracy and efficiency in data analysis. Read the full research article by Amir Reza Salehi & Majid Khedmati, published in Scientific Reports on 02 March 2024, here: https://lnkd.in/d4tNWTgC What are your thoughts on the impact of such algorithms on the field of data science? Share your thoughts below and follow us on LinkedIn for more scientific insights every week! #WeeklyPublication #ScientificResearch #ScientificReports #Research #Multiomics #MachineLearning #Innovation #ImbalancedData #DataScience #CSBBoost #Classification #BigData #BalancedData #ArtificialIntelligence #AI #Algorithm
A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data - Scientific Reports
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"Pioneering Creativity and Innovation" I am a polymath—a lifelong learner with a deep and diverse understanding across multiple fields.
HEMANTH LINGAMGUNTA Unleashing the Power of Waves: How 1D Gaussian Wave Packets are Revolutionizing AI Training and Unlocking Hidden Patterns in Data through Fourier Analysis, Propelling LLMs, VLMs, and APIs to New Heights of Performance and Understanding. What is a 1D Gaussian Wave Packet? A 1D Gaussian Wave Packet is a mathematical representation of a wave packet whose envelope is shaped like a Gaussian function. This means that the amplitude of the wave packet is highest at the center and gradually tapers off towards the edges[1][2]. This non-dispersive and localized nature makes Gaussian wave packets particularly useful in physics and engineering. Applications in AI Training 1. Signal Processing: Fourier transforms, which are integral to analyzing wave packets, can be used to decompose complex signals into their fundamental components. This is crucial for tasks like audio analysis and image processing in AI[7]. 2. Feature Extraction: By converting data into the frequency domain, Fourier analysis can help extract meaningful features from signals, which is essential for training LLMs and VLMs[7]. 3. Efficiency: The use of Fast Fourier Transform (FFT) algorithms can significantly reduce computation time, making it feasible to process large datasets efficiently[7]. Integrating Gaussian Wave Packets into AI Training - LLMs: Analyzing text data in the frequency domain can help identify linguistic patterns and improve natural language understanding. - VLMs: Fourier transforms enable efficient image processing and feature extraction, enhancing computer vision capabilities. - APIs: Incorporating Fourier-based algorithms can boost the performance of machine learning APIs for tasks like audio analysis and time series forecasting. Conclusion By bridging classical signal processing with modern deep learning, Gaussian wave packets and Fourier analysis are helping to unlock new frontiers in AI and data science. As we continue to push the boundaries of artificial intelligence, these fundamental mathematical tools remain as relevant as ever. #AI #MachineLearning #DataScience #FourierAnalysis #GaussianWavePackets Citations: [1] Wave Packet: Gaussian Definition & Technique - StudySmarter https://lnkd.in/g9Gi8BNC [2] Wave packet - Wikipedia https://lnkd.in/guTz7T_z [3] CSP Blog Highlights https://lnkd.in/gpV-GNr4 [4] Gaussian function - Wikipedia https://lnkd.in/guSAwmWr [5] STATISTICAL FOURIER ANALYSIS: https://lnkd.in/gzAZyUzH [6] Modeling Earth’s Atmosphere with Spherical Fourier Neural Operators | NVIDIA Technical Blog https://lnkd.in/gZqmzX7Z [7] Fourier Transformation for a Data Scientist - KDnuggets https://lnkd.in/gB5V9KJE
Fourier Transformation for a Data Scientist - KDnuggets
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𝗨𝗻𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗧𝗶𝘁𝗮𝗻𝗶𝗰: 𝗔 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 Welcome to my latest project, where I embarked on a fascinating adventure to predict the survival rates of passengers on the ill-fated Titanic. Using the Titanic dataset, I employed a range of #MachineLearning techniques to uncover the most accurate predictor of survival. Join me as I share my journey! As I learned from respected Sir Muhammad Irfan, Dr. Sheraz Naseer - (PhD Artificial Intelligence, Data Science), and Muhammad Haris Tariq in Basic and Advanced AI Courses, after conducting advanced sessions I prepared myself with all previous stuff revision. 𝗦𝘁𝗲𝗽 𝟭: 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 I began by visualizing the dataset to understand the distribution of survival rates, passenger classes, and other key factors. This helped me identify patterns and correlations. 𝗦𝘁𝗲𝗽 𝟮: 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Next, I delved into exploratory data analysis to uncover hidden insights. I discovered that factors like age, gender, and passenger class significantly impacted survival rates. 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 I preprocessed the data by handling missing values, encoding categorical variables, and scaling numerical features. This ensured a clean and ready dataset for modeling. 𝗦𝘁𝗲𝗽 𝟰: 𝗠𝗼𝗱𝗲𝗹 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗠𝗟 I split the dataset into training and testing sets, preparing it for machine learning modeling. 𝗦𝘁𝗲𝗽 𝟱: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 I implemented five different machine learning algorithms: #LogisticRegression, #DecisionTree, #RandomForest, #GradientBoosting 𝗦𝘁𝗲𝗽 𝟲-𝟵: 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 I compared the performance of each classifier: - Logistic Regression: 82.02% accuracy - Decision Tree: 78.65% accuracy - Random Forest: 82.58% accuracy - Gradient Boosting: 84.27% accuracy (winner!) 𝗦𝘁𝗲𝗽 𝟭𝟬: 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 After a thorough comparison, Gradient Boosting emerged as the clear winner, achieving an impressive 84.27% accuracy in predicting survival rates. This project demonstrated the power of machine learning in uncovering hidden patterns and making accurate predictions. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: - Data visualization and exploratory data analysis are crucial for understanding the dataset. - Feature engineering and data preprocessing are essential for model performance. - Gradient Boosting outperformed other algorithms in this project, highlighting its effectiveness in handling complex datasets. Thanks for joining me on this machine-learning journey! If you have any questions or want to explore more, feel free to connect with me. Let's continue learning and growing together! GitHub Project Link: https://lnkd.in/dEEVgiNS #MachineLearning #GradientBoosting #DataScience #PredictiveModeling #EDA #ML #Seaborn #AI #DL
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The Poisson Distribution A Beginner's Guide for Data Scientists! 📊 The Poisson distribution is a fundamental concept in statistics that every data scientist should understand. It helps us model the probability of a specific number of events occurring within a fixed interval of time or space, especially when these events happen independently and at a constant average rate. • Definition and History: The Poisson distribution was introduced by the French mathematician Siméon-Denis Poisson in 1830. He developed this function to describe rare events, such as the number of times a gambler wins in a game of chance over many trials. This distribution is essential for analyzing situations where events occur independently and infrequently. • Parameters: The key parameter of the Poisson distribution is λ (lambda), which represents both the mean and variance of the distribution. This means that if you know the average rate of occurrence, you can predict the likelihood of different outcomes. • Usage in AI and Data Science: 1. Probabilistic Models: The Poisson distribution is often used in Generalized Linear Models (GLMs) to model count data, making it suitable for predicting outcomes like customer arrivals or service requests. 2. Event Prediction: In AI applications, it helps predict the likelihood of events over time, such as: •• Customer purchases •• Call center inquiries •• Server requests 3. Simulation: The distribution can simulate complex systems, such as predicting extreme weather events or analyzing social media interactions (e.g., Twitter messages). • Real-World Applications: Here are some everyday examples where the Poisson distribution comes into play: •• Traffic Accidents: Estimating the number of car accidents at a specific intersection over a month. •• Customer Service: Predicting how many customers will enter a shop during a busy hour. •• Healthcare: Estimating the number of emergency calls received by hospitals within a specific timeframe. Understanding the Poisson distribution equips you with valuable tools for analyzing real-world data effectively. Keep exploring and learning! 🚀 #DataScience #Statistics #PoissonDistribution #MachineLearning #Analytics #LearningJourney #AI #ML #BeingDataScientist #BDS #Innovation • Citations: [1] https://lnkd.in/dzUWRd4z [2] https://lnkd.in/dDA3iKCZ [3] https://lnkd.in/dMMJ3DYu [4] https://lnkd.in/ddfrJ4ez [5] https://lnkd.in/dfhjNitX [6] https://lnkd.in/d2ayRkwS [7] https://lnkd.in/dFzwUJHX [8] https://lnkd.in/ePrYV5NK
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Summarizing data is more than just averaging - it's about capturing the shapes that reveal true insights. 🔎 From skewness and kurtosis to discrete frequency-domain signals and embeddings, the way we mathematically represent our data opens up unique perspectives. It determines what patterns we can and cannot see. In a previous role, I was modeling physiological tremor of smartphone users. By extracting frequency domain features within sliding time windows prepared based on the Nyquist frequency, I was able to significantly boost the sensitivity of my detection system. I find it hard to believe that a million or billion parameter deep network could automatically learn such intricate characteristics supported by fundamentals of the physical world. So what data shapes or summarization techniques have been game-changers for your machine learning models? Have you ever had the 'right' mathematical summary unlock valuable insights that averages alone couldn't provide? Is this topic even relevant in today’s ML & AI landscape? Read my latest long-form article exploring this topic in depth: https://lnkd.in/gPvuTcEb Share your experiences and let's discuss how to masterfully represent the true form of our data! #deeplearning #machinelearning #exploratorydataanalysis #featureengineering #statistics #embeddings #artificialintelligence #ai
Summarizing data mathematically
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For decades, we have been so engrossed with technical aspects of data, databases, and data management that we lost our ability to see the "forest and the trees" view. This led to the massive data and applications silos that the majority of organizations today struggle with. It resulted from the classic separation of art and science that is still so widely prevalent in the world of technology. Data ontology can allow us to build the forest and trees in the same view. #data #analytics #ai #ml #datascience #designedanalytics https://lnkd.in/gEJTFr9m
Data Ontology In The Age of Gen AI
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Data scientists and simulation modelers might be similar and rely heavily on the facts in front of them, but that doesn’t mean they think or speak the same way. The trick to managing the discourse? Crystal-clear communication and dedication toward resolving key differences. Consider a few steps in the right direction. #DataAnalyticsAndModeling #SimulationModeling #AdvancedDataAnalytics #data #ai #Simulation #anylogistix https://hubs.li/Q02n5Gv20
How to Manage Discourse Between Data Scientists and Simulation Modelers
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For decades, we have been so engrossed with technical aspects of data, databases, and data management that we lost our ability to see the "forest and the trees" view. This led to the massive data and applications silos that the majority of organizations today struggle with. It resulted from the classic separation of art and science that is still so widely prevalent in the world of technology. Data ontology can allow us to build the forest and trees in the same view. #data #analytics #ai #ml #datascience #designedanalytics https://lnkd.in/gcm_wwgD
Data Ontology In The Age of Gen AI
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Executive Director, FH Medics | Co-CEO, Na Prints PLC | Innovating Healthcare through Ultrasound Training Healthcare Leader | Ultrasound Training Expert | Driving Clinical Care
8moThis article definitely throws a lot of technical terms your way, but I have to say, the section on "Vector Space and Dimensionality" really piqued my interest and made me want to learn more. It also got me thinking about the challenges of training AI, especially when it comes to healthcare. The illustration adds to the overall experience, it makes me think of Sherlock Holmes 🧐