365 Data Science’s Post

😂 I experience this a lot when training an SVM and Random Forest model using GridSearchCV. #DataScience #MachineLearning #CrossValidation #ParameterTuning

Greetings, everyone! As we already discuss the core concepts of Gradient Descent in our previous post. Today, I will share the mathematical formulation of Gradient Descent. This notes also involves a brief discussion of the types of Gradient Descent Stay tuned for next topics #MachineLearning #DataScience

#riddleanswer Appreciation goes to everyone who took a moment to engage with our post. Congratulations Millicent Omondi for getting the answer right first. #Accuracy is a common #metric in Machine Learning that measures the #proportion of correct #predictions out of the total instances. While it's simple to calculate, it can be misleading in imbalanced datasets, where a model can achieve high accuracy by favoring the majority class without correctly predicting the minority class. For a more comprehensive evaluation, metrics like #precision, #recall, or # F1-score are often needed, especially when class distribution is skewed. Congratulations once again to all those who answered correctly! #machinelearning #datascience #riddles

🎉 Excited to have presented my recent work at the CRUNCH seminar! Thanks to George Karniadakis, Nazanin(Naz) Ahmadi, and Juan Diego Toscano for the invitation and organization – it was a great experience sharing my research. You can watch the presentation on YouTube: https://lnkd.in/e9GfZJDV In the seminar, I discussed two of my latest works: 1️⃣ Variational Physics-Informed Neural Operator (VINO) for Solving Partial Differential Equations Check out the paper here: arxiv.org/abs/2411.06587 2️⃣ DeepNetBeam: A Framework for the Analysis of Functionally Graded Porous Beams Find the details here: arxiv.org/abs/2408.02698 Looking forward to more insightful discussions on neural operators and scientific machine learning. Let’s keep pushing the boundaries of machine learning in scientific computing! #NeuralOperators #MachineLearning #PDE #CRUNCH #CRUNCHSeminar #DeepNetBeam #VINO

🌸 Exciting News! Just completed my first project on Iris Flower Classification at CipherByte Technologies! 🌿📊 🔍 Using machine learning algorithms like k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), I successfully classified iris flowers into species based on their sepal and petal measurements. 🚀 This project was a fantastic learning experience, from data exploration and model training to evaluating performance metrics like accuracy and confusion matrices. 📈 Achieved results: k-NN Accuracy: 100% SVM Accuracy: 100% Detailed classification reports and insightful visualizations. 🌐 Excited to share this milestone with my network and showcase the capabilities of machine learning in solving real-world problems! Check out on github: https://lnkd.in/g9ktuApV #MachineLearning #DataScience #IrisClassification #PythonProgramming #CipherByteTechnologies

🌟 Excited to announce the release of our latest blog post on "Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight" available on arXiv. Learn about the powerful tool of sparse regression applied to symbolic libraries for learning governing equations directly from data. The post presents a general purpose, model agnostic sparse regression algorithm, SPRINT, that utilizes bisection with analytic bounds for rapid identification of null vectors, maintaining sensitivity to small coefficients while being computationally efficient for large symbolic libraries. Discover how this accelerated scheme is revolutionizing model discovery. Read the full post here: https://bit.ly/3V3WPKx #ModelDiscovery #SparseRegression #DataScience

Decision Tree Classifier is a supervised machine learning algorithm which is used in classification to classify the dataset. In this task a Decision Tree Classifier is used to classify the types of flowers. #MachineLearning #GRIPMAY24 #DataScience The Sparks Foundation

K-Means is an important tool to deal with the classification problem. It is an supervised machine learning algorithm which can make the clusters of your datasets on the basis of similarities between the data. The number of cluster for best prediction of your dataset is got by finding the elbow point. In this task I have trained a model using the k-means technique which can predict the type of flower on the basis of its input features. #GRIPMAY24 #MachineLearning #DataScience The Sparks Foundation

Condensing Random Forest into Single Decision Tree 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dDgjMisc Random Forest is a popular ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of a model. By aggregating the predictions of multiple trees, Random Forest can effectively reduce overfitting and improve the overall performance of a model. However, in some cases, it may be useful to condense the complexity of Random Forest into a single decision tree. This can be achieved by aggregating the feature importance scores and split decisions of the individual trees to create a single, more interpretable tree. The process of condensing a Random Forest into a single decision tree involves several steps, including aggregating the feature importance scores, selecting the most important features, and creating a single decision tree using the aggregated feature importance scores. By following these steps, it is possible to create a single decision tree that captures the essential decision-making power of the original Random Forest. Random Forest is a widely used machine learning technique, particularly in domains where ensemble methods are used to improve model accuracy. For those interested in exploring more advanced machine learning techniques, we suggest taking a closer look at some of the latest research papers on Random Forest and its applications. Additionally, we recommend experimenting with different variants of Random Forest, such as Gradient Boosting or Extremely Randomized Trees, to see how they perform on different datasets. Additional Resources: * https://lnkd.in/dWqU3QvJ * https://lnkd.in/d3jVhKjE #RandomForest #DecisionTrees #MachineLearning #EnsembleMethods #DataScience #Stem #LearningFromData #ArtificialIntelligence Find this and all other slideshows for free on our website: https://lnkd.in/dDgjMisc #RandomForest #DecisionTrees #MachineLearning #EnsembleMethods #DataScience #Stem #LearningFromData #ArtificialIntelligence https://lnkd.in/d8a8SXq9

Today, I delved into Problem No. 179: Topological Sort as part of my DSA challenge. 🌟 Problem Overview:   The task was to perform a topological sort on a Directed Acyclic Graph (DAG). A topological sort is a linear ordering of vertices such that for every directed edge from vertex ‘u’ to vertex ‘v’, vertex ‘u’ appears before ‘v’ in the ordering. Approach:   I utilized Kahn's algorithm, which is a popular method for topological sorting that involves: 1. Calculating the in-degree of each vertex. 2. Using a queue to process vertices with zero in-degrees, ensuring the correct order of traversal. 3. Adding processed vertices to the result array and updating the in-degrees of adjacent vertices. Key Takeaways:   1. Understanding the properties of DAGs is crucial for solving problems related to topological sorting.   2. Kahn's algorithm provides an efficient way to achieve topological ordering with a clear approach using in-degrees.   3. Multiple valid topological sorts exist for a single DAG, which can be beneficial in various applications. Personal Reflection:   This exercise solidified my understanding of graph algorithms and their applications in scheduling tasks, project planning, and more. I look forward to applying these concepts in future projects! 🚀 If anyone has insights or additional methods for topological sorting, I’d love to hear about them! #DataStructures #Algorithms #GraphTheory #TopologicalSort #DSAChallenge