Supervised Learning

KNN Algorithm Explained for Beginners | Classification, Regression & Distance Metrics

Welcome to MatrixMind SK! In this video, we dive into the K-Nearest Neighbors (KNN) Algorithm, one of the most intuitive and beginner-friend...

Supervised Learning: Tasks, Algorithms, and Metrics

How Do ML Evaluation Metrics Affect Algorithm Performance? - AI and Machine Learning Explained

How Do ML Evaluation Metrics Affect Algorithm Performance? Are you curious about how the effectiveness of machine learning models is measure...

How Do You Choose Evaluation Metrics For ML Algorithms? - AI and Machine Learning Explained

How Do You Choose Evaluation Metrics For ML Algorithms? Are you curious about how to evaluate the performance of your machine learning model...

Scikit-Learn Full Crash Course - Python Machine Learning

Today we to a crash course on Scikit-Learn, the go-to library in Python when it comes to traditional machine learning algorithms (i.e., not....

Logistic Regression (and why it's different from Linear Regression)

Gentle Introduction to Logistic Regression. We explore this powerful yet simple machine learning model for binary classification tasks like....

K-nearest Neighbors (KNN) in 3 min

Visual Introduction to K-nearest Neighbors (KNN) for classification problems in Machine learning. -------------------------- This video woul...

What is a Loss Function? Understanding How AI Models Learn

Download the AI Foundation model ebook to learn more → https://ibm.biz/BdGsJd Learn more about the Loss Functions here → https://ibm.biz/BdG...

All Machine Learning algorithms explained in 17 min

All Machine Learning algorithms intuitively explained in 17 min ######################################### I just started my own Patreon, in....

Supervised Learning | Classification and Regression | Machine Learning Tutorial | Tutorialspoint

Supervised Learning for Beginners. In this 'Machine learning tutorial', you will learn about Supervised Learning, Classification and Regress...

Lec-23: Supervised vs Unsupervised learning with real life example

Supervised and Unsupervised learning are both essential in machine learning, but they serve different purposes. In this video, we’ll break d...

Lec-2: Supervised Learning Algorithms | Machine Learning

Supervised Learning algorithms are a key part of machine learning, where models are trained on labeled data to make predictions or classify....

What are the Metrics used to Evaluate the performance of Regression Models in ML DM by Mahesh Huddar

What are the Metrics used to Evaluate the performance of Regression Models in Machine Learning Data Mining by Mahesh Huddar The following co...

Supervised vs. Unsupervised Learning

Learn more about WatsonX: https://ibm.biz/BdPuCJ More about supervised & unsupervised learning → https://ibm.biz/Blog-Supervised-vs-Unsuperv...

What is Random Forest?

Learn about watsonx: https://ibm.biz/BdvxRb Can't see the random forest for the search trees? What IS a "random forest" anyway? IBM Master I...

How to evaluate ML models | Evaluation metrics for machine learning

There are many evaluation metrics to choose from when training a machine learning model. Choosing the correct metric for your problem type a...

Machine Learning Tutorial (3/25) : Evaluation Metrics for Supervised Learning

Metrics are measures used in Machine Learning to evaluate the performance of a model by comparing the predicted values with the actual value...

ML 3: Supervised Learning with Examples | Regression VS Classification

Details About: Types of Learning Introduction of Supervised Learning Working of Supervised Learning Algorithm Steps of Supervised Learning A...

Evaluating Classification and Regression Machine Learning Models

===== Likes: 23 👍: Dislikes: 0 👎: 100.0% : Updated on 01-21-2023 11:57:17 EST ===== Interested in what Machine Learning Metrics are applic...

Learning to Rank - The ML Problem You've Probably Never Heard Of

You've heard of regression and classification ... but have you heard of this? My Patreon : https://www.patreon.com/user?u=49277905

Stanford CS229: Machine Learning | Summer 2019 | Lecture 21 - Evaluation Metrics

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3b2QxDe Anand A...

#68: Scikit-learn 65:Supervised Learning 43: Distance metrics (1/3)

The video discusses the intuition for distances in boolean-valued vector spaces. Additionally, the video also talks about user-defined funct...

Scikit-learn Crash Course - Machine Learning Library for Python

Scikit-learn is a free software machine learning library for the Python programming language. Learn how to use it in this crash course. ✏️ C...

Cornell CS 5787: Applied Machine Learning. Lecture 20. Part 3: Advanced Classification Metrics

Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression

In this Python machine learning tutorial for beginners, we will look into, 1) What is overfitting, underfitting 2) How to address overfittin...

Supervised vs Unsupervised vs Reinforcement Learning | Machine Learning Tutorial | Simplilearn

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Precision, Recall, F1 score, True Positive|Deep Learning Tutorial 19 (Tensorflow2.0, Keras & Python)

In this video we will go over following concepts, What is true positive, false positive, true negative, false negative What is precision and...

Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai This lecture...

Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1

Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more ht...

Machine Learning Fundamentals: The Confusion Matrix

One of the fundamental concepts in machine learning is the Confusion Matrix. Combined with Cross Validation, it's how we decide which machin...

11. Introduction to Machine Learning

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instruc...

Machine Learning: Testing and Error Metrics

Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt A friendly journey into th...

AliGhodsi Lec 12, Metric Learning

stats441 feb9 2017, Metric Learning, Hilbert-Schmidt Independence Criterion