Ensemble Learning says, if we can build multiple models then why to select the best one why not top 2, again why not top 3 and why not top 10. Then if you find top 10 deploy all 10 models. And when new data comes, make a prediction from all 10 models and combine the predictions and finally make a joint prediction. This is the key idea of ensemble learning.
It does not matter how much experience you have, actually anybody can start or switch to data science and machine learning. The only important this is, how much eager you are for it. What it means to you. If you are very much keen to work in this field then nobody can stop you. There might be some short term hurdles however if you are focused enough and know your goals regarding where you want to see yourself after certain years, then you will definitely be successful in overcoming those hurdles.
Bayes Theorem is the extension of Conditional probability. Conditional probability helps us to determine the probability of A given B, denoted by P(A|B). So Bayes’ theorem says if we know P(A|B) then we can determine P(B|A), given that P(A) and P(B) are known to us.
Conditional Probability helps Data Scientists to get better results from the given data set and for Machine Learning Engineers, it helps in building more accurate models for predictions.
Probability is used to predict the likelihood of the future event.
Statistics is used to analyse the past events
Probability tells us what will happen in a given ideal world?
While Statistics tells about how ideal is the world?
Probability is the basics of Inferential Statistics.
Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.