Fatima Jannetmahia.hashnode.dev·Nov 4, 2024ML Classification 3.5: Naive BayesHello and welcome back to the blog of machine learning. Today we will learn about Bayes theorem. Our main focus for this blog is on naive but we can’t proceed to it without Bayes theorem so here it is. Question Why is this algorithm called the native...Discuss·2 likes·37 readsMachine Learning (Python)Machine Learning
Fatima Jannetmahia.hashnode.dev·Nov 1, 2024ML Classification 3.4: Kernel SVMHello and welcome back to machine learning. Previously we learned about linear support vector machine algorithm. Today we’ll learn the kernel support vector algorithm. Let’s start Kernel SVM Intuition As you can recall, in the support vector machine ...Discuss·1 like·54 readsMachine Learning (Python)Machine Learning
Fatima Jannetmahia.hashnode.dev·Oct 30, 2024ML Classification: Support Vector Machine (SVM)Support Vector Machines (SVMs) were initially developed in the 1960s and refined in the 1990s. Currently, they are becoming very popular in machine learning because they have demonstrating that they are very powerful and somewhat different from other...Discuss·3 likes·37 readsMachine Learning (Python)Machine Learning
Fatima Jannetmahia.hashnode.dev·Oct 29, 2024ML Classification: K-NN (K-Nearest Neighbor)Hello and welcome back to the ML blogs. Today we will learn about the K nearest neighbor. Let’s get started! Intuition Let’s say you have a plot where you have two types of category, red data and green data. Now, if a new data point appears where sho...DiscussMachine Learning (Python)K-NN
Victor Uzoagbavictoru.hashnode.dev·Oct 29, 2024From Jupyter to Production: Streamlining ML Workflows in Saturn CloudAs machine learning (ML) workflows mature, so does the need for efficient, scalable production processes. While Jupyter notebooks have revolutionized the ML development phase, transitioning from experimentation to production presents unique challenge...Discussmachine learning models
Victor Uzoagbavictoru.hashnode.dev·Oct 28, 2024Building an ML Pipeline with Kubeflow: From Development to ProductionMachine Learning (ML) workloads in production require robust, scalable, and maintainable pipelines. Kubeflow provides a comprehensive solution for deploying ML workflows on Kubernetes, enabling data scientists and ML engineers to focus on model devel...DiscussMachine Learning
Fatima Jannetmahia.hashnode.dev·Oct 26, 2024Machine Learning Regression Model Selection in PythonHello, welcome back to ML! So far, we have covered regressions, and this blog is about choosing the right regression model. Which one should you apply to your model? Which one you should choose? You'll find all your answers in this blog. I can confid...Discuss·37 readsMachine Learning (Python)Python
Fatima Jannetmahia.hashnode.dev·Oct 25, 2024Machine Learning Chapter 2.6: Random Forest RegressionHello and welcome back to Machine Learning! Today, we'll learn about the intuition behind random forests and how to apply them step by step in Python. Let's get started. This will be our final blog on regression. Intuition Random forest is a version ...Discuss·54 readsMachine Learning (Python)Python
Fatima Jannetmahia.hashnode.dev·Oct 25, 2024Machine Learning Chapter 2.5: Decision Tree RegressionWelcome to another blog on Machine Learning! Today we are going to have a look at the Decision Tree Regression. Intuition There is a term called CART which stand for classification and regression tree. In this blog we’ll talk mostly about regression ...DiscussMachine Learning (Python)ML
Fatima Jannetmahia.hashnode.dev·Oct 23, 2024Machine Learning Chapter 2.4: Support Vector Regression (SVR)Welcome to Machine Learning! Support vector regression was created in the 90s by Vladimir Vapnik and his team at Bell Labs, which was known as AT&T Bell Labs back then. But for now, we'll focus just on linear regression. Let's get started! SVR Intuit...DiscussMachine Learning (Python)Machine Learning
