Om Prakash Acharya的动态

?? New Article, LinkedIn Community! ?? I'm thrilled to share my latest article on Medium: "Navigating Machine Learning Datasets: Test, Validation, and Training" ??. Read it here: https://lnkd.in/dmR7eRcF In this piece, I dive deep into machine learning datasets and explore their fascinating components. ??? If you're passionate about {AI (artificial intelligence or ML machine learning), I'd love for you to check it out and let me know your thoughts! ?? Your feedback and insights are invaluable to me. You can find this article and more of my work on my Medium blog: https://lnkd.in/d2xvUbFc ?? Thank you for your continuous support! ?? Your partner in code, Roscode ?? #machinelearning #ai #ml #datasets

Week 7: Introduction to Machine Learning I have covered Evaluation and Evaluation Measure, Bootstrapping and Cross Validation, 2 class Evaluation measure, ROC curve, Minimum Description Length and Exploratory Analysis, Ensemble Methods - Bagging, Committee Machines and stacking, Boosting. ???? Let's connect and delve into the world of ML algorithms. #MachineLearning #DataScience #LearningJourney #Algorithm

?? Just wrapped up Chapter 4 of?Hands-On Machine Learning! ?? This chapter was a deep dive into some powerful regression techniques. I explored: Linear Regression?and its foundational concepts. Stochastic Gradient Descent (SGD)?for efficient optimization. Batch SGD?and its trade-offs. Accelerated SGD?for faster convergence. Polynomial Regression?to model nonlinear relationships. Lasso Regression?for regularization and feature selection. It’s amazing how these algorithms balance complexity and performance to solve real-world problems. Excited to apply these concepts to datasets and see them in action! ?? If you’ve worked with these techniques, I’d love to hear your insights or tips. Let’s connect and geek out over ML! ?? Here is link for my work: https://lnkd.in/di-HJHaU #MachineLearning #DataScience #Regression #SGD #PolynomialRegression #Lasso #HandsOnML #AI #LearningInProgress

The generative model is to learn the underlying distribution of the training data, allowing it to generate new data samples that are similar in structure and distribution to the original data.Unleash the power of Machine learning synthetic data in Ml field. #infocylanz #machinelearning #MLprompts

The generative model is to learn the underlying distribution of the training data, allowing it to generate new data samples that are similar in structure and distribution to the original data.Unleash the power of Machine learning synthetic data in Ml field. #infocylanz #machinelearning #MLprompts

Machine Learning: Understanding Hinge Loss and Its Role in SVM Optimization ???? GET FULL SOURCE CODE AT THIS LINK ???? ?? https://lnkd.in/dbiSDKN9 Machine Learning has become a cornerstone of modern technology, allowing systems to learn from data and make predictions or decisions. In this explanation, we will dive into the concept of Hinge Loss and its significance in Support Vector Machines (SVM) optimization. Hinge Loss is a popular loss function in Machine Learning, particularly in the context of SVMs. Its main goal is to find the best-fit hyperplane that maximally separates data points from different classes. The function introduces a margin, quantifying the distance between the hyperplane and the closest data points from each class. When the margin is large, the model is considered to have a good generalization ability. Additionally, Hinge Loss employs soft-margin techniques, which allow some data points to lie on the wrong side of the hyperplane. These misclassifications, however, are penalized with a loss. By minimizing the Hinge Loss, we optimize the SVM model to find the hyperplane that separates the data points efficiently while maximizing the margin. It is essential to understand the underlying mathematics and meaning of Hinge Loss to take full advantage of the SVM's robustness and versatility. Those seeking to delve deeper into this topic are encouraged to explore the following resources: - [SVM: A Review](https://lnkd.in/dE8EP8fb) - [On Support Vector Methods for Function Approximation, Regression Estimation, and Signal Processing](https://lnkd.in/dyKubkxV) Additional Resources: [Optional] #STEM #Programming #MachineLearning #SupportVectorMachines #SVM #HingeLoss #Optimization #DataScience #Technology #AI #predictiveanalytics #datasciencecommunity Find this and all other slideshows for free on our website: https://lnkd.in/dbiSDKN9 #STEM #Programming #MachineLearning #SupportVectorMachines #SVM #HingeLoss #Optimization #DataScience #Technology #AI #predictiveanalytics #datasciencecommunity https://lnkd.in/dQay8vEb

Unlock the power of Machine Learning! ?? Join our 4-Day Virtual Workshop to gain hands-on experience in ML, from fundamentals to advanced applications. Learn, implement, and elevate your research skills with expert guidance. ?? ?? Starts: January 6, 2025 ?? Mode: Online (Google Meet) ?? Register Now: https://lnkd.in/g6PMdj3n #MachineLearning #AIWorkshop #MLForResearch #DataScience #AIinAcademia #DeepLearning #ResearchSkills #AcademicTools #ArtificialIntelligence #MLModels #HandsOnTraining #AIApplications #ResearchInnovation #VirtualWorkshop #MachineLearningBasics #AdvancedML #ModelOptimization #PythonForAI #AIInResearch #DataPreparation #MLTraining #Streamlit #FlaskForAI #ResearchDevelopment #MLFundamentals

?? Demystifying Machine Learning Models! ?? I’m thrilled to share my latest presentation: "Types of Machine Learning Models." This deck provides a comprehensive introduction to the foundational categories of ML models and their applications, including: ? Supervised Learning - Exploring algorithms like Linear Regression, Decision Trees, and more ? Unsupervised Learning - Diving into techniques such as Clustering and Dimensionality Reduction ? Semi-Supervised Learning - Bridging the gap between labeled and unlabeled data ? Reinforcement Learning - Understanding how agents learn through trial and error This presentation is ideal for those looking to understand the core concepts and algorithms powering modern machine learning. #MachineLearning #ArtificialIntelligence #Tech #DataScience #MLModels #GenAI #LLM

?? Just completed Chapter 5 of?Hands-On Machine Learning! ?? This chapter was all about?Support Vector Machines (SVMs)—a powerful and versatile tool for both classification and regression tasks. Here’s what I explored: Linear SVM Classification: Understanding how SVMs find the optimal hyperplane to separate data with the maximum margin. Nonlinear SVM Classification: Using kernels to transform data into higher dimensions, making it possible to classify complex, non-linear datasets. Kernels: Diving into polynomial kernels, Gaussian RBF kernels, and others to handle different types of data patterns. Grid Search: Leveraging?GridSearchCV?more effectively to fine-tune hyperparameters and optimize model performance. Optimization Techniques: Gaining insights into the mathematical optimizations behind SVMs, including the hinge loss function and dual problem formulation. What fascinates me most is how SVMs balance precision and generalization, especially with the right kernel and hyperparameters. The ability to handle both linear and non-linear problems makes SVMs a go-to algorithm for many real-world challenges. I’m excited to apply these concepts to real datasets and see how SVMs perform compared to other models I’ve learned so far. If you’ve worked with SVMs or have tips on kernel selection, hyperparameter tuning, or practical use cases, I’d love to hear your thoughts! Let’s connect and keep the learning journey going! ?? Here is link for my work: https://lnkd.in/di-HJHaU #MachineLearning #DataScience #SVM #Kernels #GridSearch #Optimization #HandsOnML #AI #LearningInProgress