Linear Regression vs Maximum Likelihood #machinelearning #statistics #datascience

📚 *RECOMMENDED BOOKS TO START WITH MACHINE LEARNING* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ If you're new to ML, here are the 3 best books I recommend (I've personally read all of these): 1. Hands-On Machine Learning – the go-to practical ML guide: https://amzn.to/3UcGqSS 2. Mathematics for Machine Learning – deep dive into the theoretical aspects of ML: https://amzn.to/3IZgHe7 3. Designing Machine Learning Systems - practical strategies for building scalable ML solutions: https://amzn.to/4ojEqFX These are affiliate links, so buying through them helps support the channel at no extra cost to you — thanks 🙏 *Summary* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video, we explore why the least squares method is closely related to the Gaussian distribution. Simply put, this happens because it assumes that the errors or residuals in the data follow a normal distribution with a mean on the regression line. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Why We Don't Use the Mean Squared Error (MSE) Loss in Classification: https://youtu.be/bNwI3IUOKyg The Bessel's Correction: https://youtu.be/E3_408q1mjo Gradient Boosting with Regression Trees Explained: https://youtu.be/lOwsMpdjxog P-Values Explained: https://youtu.be/IZUfbRvsZ9w Kabsch-Umeyama Algorithm: https://youtu.be/nCs_e6fP7Jo Eigendecomposition Explained: https://youtu.be/ihUr2LbdYlE *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic https://twitter.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #svd #singularvaluedecomposition #eigenvectors #eigenvalues #linearalgebra