Had a thought about under pinning these newer videos. Like the reference to other videos it gives a different perspective in understanding the topic. Or maybe the use case may change. Love my morning IBM videos and appreciate all of them. You are the expert developer advocates and huge respect to the hosts. There is so much to learn but underpinning with multiple videos that may strengthen the concept. This video explained what but how is another video for example and for the audience another way to say this may be helpful. Just a thought not sure I’m being clear but just wanted to comment about the thought. Keep up the great work in educating us on key concepts and technologies team IBM. Thank you
Understanding Bias and Variance: The key challenges in optimizing machine learning models include addressing bias and variance to prevent overfitting and underfitting. 📉 Bias Explanation: Bias occurs when models oversimplify and fail to capture underlying patterns, leading to underfitting. 📈 Variance Dilemma: High variance leads to models that memorize data points rather than learning patterns, resulting in overfitting. 🔄 Bias-Variance Tradeoff: Achieving low bias and low variance is crucial for creating effective models that generalize well beyond training data. 📊 Ideal Model Complexity: The goal is to find a model complexity that minimizes both bias and variance, optimizing performance.
One of the best simple explanations I've ever seen in AI and Machine Learning! Thank you!
Had a thought about under pinning these newer videos. Like the reference to other videos it gives a different perspective in understanding the topic. Or maybe the use case may change. Love my morning IBM videos and appreciate all of them. You are the expert developer advocates and huge respect to the hosts. There is so much to learn but underpinning with multiple videos that may strengthen the concept. This video explained what but how is another video for example and for the audience another way to say this may be helpful. Just a thought not sure I’m being clear but just wanted to comment about the thought. Keep up the great work in educating us on key concepts and technologies team IBM. Thank you
i looovveee this girl, i love the way she speaks and explains. wish to see more videos of you
Good simple explanation and examples.
Thank you for this Segment . I am working my way into becoming a Data Scientist.
Effective and clear explanation. Thank you!
Understanding Bias and Variance: The key challenges in optimizing machine learning models include addressing bias and variance to prevent overfitting and underfitting.
📉 Bias Explanation: Bias occurs when models oversimplify and fail to capture underlying patterns, leading to underfitting.
📈 Variance Dilemma: High variance leads to models that memorize data points rather than learning patterns, resulting in overfitting.
🔄 Bias-Variance Tradeoff: Achieving low bias and low variance is crucial for creating effective models that generalize well beyond training data.
📊 Ideal Model Complexity: The goal is to find a model complexity that minimizes both bias and variance, optimizing performance.
Thank you
so much learning in 4 minutes