Maximum Likelihood - Explained

Maximum Likelihood Estimation (MLE) is a fundamental concept in statistics and machine learning used to estimate model parameters from data. This video explains maximum likelihood estimation intuitively using the normal distribution, showing how parameters like the mean (μ) and standard deviation (σ) are chosen to best fit observed data. Learn how likelihood works, why the log-likelihood is used in practice, and the key difference between probability and likelihood in statistical modeling. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ K-Means Clustering: https://youtu.be/dyG9cj5RKL0 Support Vector Machines: https://youtu.be/K1EcCjDD_q4 The Hessian Matrix: https://youtu.be/9tp1kULwU2w The Jacobian Matrix: https://youtu.be/6FesMicc844 Bayesian Optimization: https://youtu.be/Kq6_kzlwSUQ Hyperparameters Tuning: Grid Search vs Random Search: https://youtu.be/G-fXV-o9QV8 The Kernel Trick: https://youtu.be/N_RQj4OL1mg Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA Dropout - Explained: https://youtu.be/FDF_Q3_98GQ Overfitting vs Underfitting: https://youtu.be/B9rhzg6_LLw Why Models Overfit and Underfit - The Bias Variance Trade-off: https://youtu.be/5mbX6ITznHk Least Squares vs Maximum Likelihood: https://youtu.be/WCP98USBZ0w *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/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): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyun