데이터 사이언스 썸네일형 리스트형 Probability & Statistics for Machine Learning & Data Science (17) Sampling and Point estimationThis week shifts its focus from probability to statistics. You will start by learning the concept of a sample and a population and two fundamental results from statistics concerning samples and populations: the law of large numbers and the central limit theorem. In lesson 2, you will learn the first and the simplest method of estimation in statistics: point estimatio.. 더보기 Probability & Statistics for Machine Learning & Data Science (16) Describing probability distributions and probability distributions with multiple variablesProbability Distributions with Multiple VariablesCovariance of a DatasetTo capture the relation between variables using covariance and correlation. How might you compare the relationship between a child's age (X) and other variables such as height (Y1), test grades (Y2), and number of naps per day(Y3)?더보기Cr.. 더보기 Probability & Statistics for Machine Learning & Data Science (15) Describing probability distributions and probability distributions with multiple variablesProbability Distributions with Multiple VariablesJoint Distribution (Discrete) - Part 1Here we have two histograms of children’s age and height (two variables).Given the following dataset, what is the probability that a child is 9 years old and 49 inches tall?더보기0.3Getting the count and probabilities in a t.. 더보기 Probability & Statistics for Machine Learning & Data Science (14) Describing probability distributions and probability distributions with multiple variablesQuizQ1Consider the four sets of samples above. Which one has the smallest variance?$$ \begin{array}{c|cccc}\hline \text { Set } & \text { Values } & & & \\ \hline 1 & 1 & 5 & 7 & 9 \\2 & -20 & -10 & 0 & 10 \\3 & 100 & 101 & 102 & 103 \\4 & -10 & -5 & 0 & -5 \\\hline\end{array} $$1234Answer더보기3The variance m.. 더보기 Probability & Statistics for Machine Learning & Data Science (13) Describing probability distributions and probability distributions with multiple variablesDescribing DistributionsQuantiles and Box-PlotsWe need to look at the data not only numerically but also visually.Visualizing data: Box-PlotsWe can visualize the data using the quantiles and we need minimum, maximum, 25%, 50%, and 75% quartiles, and the interquartile range (IQR) which is the third quartile .. 더보기 Probability & Statistics for Machine Learning & Data Science (12) Describing probability distributions and probability distributions with multiple variablesDescribing DistributionsSkewness and Kurtosis: Moments of a DistributionWhen we multiply the random variable X with the power of an integer n, we call it the nth moment.Skewness and Kurtosis - SkewnessWithout calculating, intuitively compare the expected value and the variance of two games. Which of the fol.. 더보기 Probability & Statistics for Machine Learning & Data Science (11) Describing probability distributions and probability distributions with multiple variablesDescribing DistributionsVarianceExpected values alone don’t tell us the whole story about the distributions as one can be wider or narrower than the other.We know the spread of data through variance.Q1: What is the maximum amount of money you should be willing to pay to play this game? Remember, you are fli.. 더보기 Probability & Statistics for Machine Learning & Data Science (10) Describing probability distributions and probability distributions with multiple variablesThis week you will learn about different measures to describe probability distributions and any dataset. These include measures of central tendency (mean, median, and mode), variance, skewness, and kurtosis. The concept of the expected value of a random variable is introduced to help you understand each of .. 더보기 이전 1 2 3 4 5 ··· 8 다음
