What are bias and variance in machine learning?
What are Bias and Variance?
Let's start with some basic definitions:

Bias: it's the difference between average predictions and true values.
Variance: it's the variability of our predictions, i.e. how spread out your model predictions are.

They can be understood from this image:

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What to do about bias and variance?
If your model suffers from a bias problem you should increase its power. For example, if the prediction of your neural network is not good enough, add more parameters, add a new layer making it deeper, etc.
If your model suffers from a variance problem instead, the best possible solution is coming from ensembling. Ensembles of Machine Learning models can significantly reduce the variance in your predictions.
The Bias-Variance tradeoff
If your model is underfitting, you have a bias problem, and you should make it more powerful. Once you made it more powerful though, it will likely start overfitting, a phenomenon associated with high variance. For that reason, you must always find the right tradeoff between fighting the bias and the variance of your Machine Learning models.

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Learning how to do that is more an art than a science!