Please explain bias and variance in relation to machine learning.
In machine learning, bias and variance are two sources of error that can affect the performance of a model.

Bias refers to the error that is introduced by approximating a real-life problem with a simplified model. A model with high bias tends to oversimplify the data, and it can lead to poor predictions, as it misses the complex relationships that exist in the data. High bias can cause underfitting, which means that the model cannot capture the underlying patterns in the data.

Variance, on the other hand, refers to the error that is introduced by the model's sensitivity to the training data. A model with high variance fits the training data very well, but it does not generalize well to new, unseen data. This is because the model has overfit the training data, memorizing the noise and outliers in the data instead of the underlying patterns. High variance can lead to poor predictions, as the model cannot extrapolate beyond the training data.

Ideally, we want a model with low bias and low variance, which can generalize well to new, unseen data. This is often referred to as the bias-variance tradeoff, where we try to find the optimal balance between the two sources of error. This can be achieved by selecting an appropriate algorithm, tuning the model's hyperparameters, and selecting a training dataset that is representative of the problem at hand.
What are some good free online resources I can use to understand more about how AI and machine learning works?
Coursera: Coursera offers a wide range of free courses on AI and machine learning, including courses from top universities like Stanford, MIT, and the University of Michigan. You can audit these courses for free or pay to earn a certificate.

edX: edX is another online learning platform that offers free courses on AI and machine learning from top universities, including Harvard and Berkeley.

TensorFlow: TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. They have a website with extensive documentation, tutorials and examples on how to use TensorFlow for machine learning.

Machine Learning Mastery: This is a popular blog and website created by Jason Brownlee, a machine learning expert and author of several books on the topic. The website offers a variety of free resources, including tutorials, guides, and sample code.

Khan Academy: Khan Academy offers a series of free videos on AI and machine learning that cover topics such as neural networks, deep learning, and decision trees.

Google AI Education: Google has created an online platform to offer educational resources and research programs to help learn more about AI and machine learning.

These are just a few examples of the many free online resources available to learn more about AI and machine learning.
What about some Videos? can you find me a link from Rumble?