Student's t-Distribution - Explained
In this video, we break down the t-distribution, a key concept in statistics used when estimating population parameters from small samples. You'll learn what it is, why it matters, and how it's different from the normal distribution, all explained with intuitive real-world examples and historical context. Perfect for students, data analysts, and anyone curious about statistical reasoning! Related Videos ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Bayesian Optimization: • Bayesian Optimization Hyperparameters Tuning: Grid Search vs Random Search: • Hyperparameters Tuning: Grid Search vs Ran... The Kernel Trick: • The Kernel Trick Cross-Entropy - Explained: • Cross-Entropy - Explained Dropout - Explained: • Dropout in Neural Networks - Explained Overfitting vs Underfitting: • Overfitting vs Underfitting - Explained Why Models Overfit and Underfit - The Bias Variance Trade-off: • Bias-Variance Trade-off - Explained Least Squares vs Maximum Likelihood: • Least Squares vs Maximum Likelihood XGBoost Explained in Under 3 Minutes: • XGBoost Explained in Under 3 Minutes Contents ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Intro 00:20 - Usage intuition 01:39 - t-Distribution invention 02:28 - t-Statistics 02:46 - t-Distribution shape 04:33 - Rule of thumb 05:21 - Practical implications 06:00 - When to use which 06:42 - Where t-distribution shows in 07:18 - Summary 08:05 - Outro Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic / datamlistic 📸 Instagram: @datamlistic / datamlistic 📱 TikTok: @datamlistic / datamlistic 👔 Linkedin: / 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: / datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #tSNE #DataVisualization #MachineLearning #DimensionalityReduction #DeepLearning

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