Central Limit Theorem
Learn Cental Limit Theorem to ace Statistics and Machine Learning concepts.
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About this course
The central limit theorem is an often applied but misunderstood vertical from statistics and machine learning domains of computer science. The theorem, although it may seem esoteric to beginners, has important implications on how and why machine learning skills and models make inferences, like if one model is statistically performing better than the others and sure intervals on model skills. In this free Central Limit Theorem course, you will discover the concepts of the central limit theorem in detail and the implications of this vital pillar of statistics and probability on applied machine learning.
Course Outline
This section defines the hypothesis and discusses the null and alternative hypotheses and their application in Machine Learning and Business Intelligence.
In this module, you will be learning the concept of the Central Limit Theorem and understanding the importance of this concept in the field of statistics.
In this module, you will be learning the application of the Central Limit Theorem on Jupyter Notebook using the numpy and pandas libraries.
In this module, you will be learning about Type-I and Type-II errors and how these errors are created in the hypothesis.
In this module, you will be learning different types of hypotheses based on the number of samples or whether it's a single-tailed test or a two-tailed test.
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Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
What is the central limit theorem, and why is it important?
In most circumstances, the Central Limit Theorem allows us to confidently assume that the sampling distribution of the mean will be normal. Statistical techniques can be used based on the assumption of a normal distribution.
How do you use the Central Limit Theorem?
The central limit theorem asserts that if you collect sufficient enough random samples with replacement from a population with a mean and standard deviation, the distribution of the sample means will be nearly normally distributed.
How do you prove the central limit theorem?
The MGF of our sampling estimator S* converges pointwise to the MGF of a standard normal RV Z, which will be used to prove the CLT. As a result, we have demonstrated that S* converges in distribution to Z, which is the CLT, and our argument is complete.
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