Some examples from this set of practice questions
1.
What is 1. Basic Terminologies and Importance in Statistics
Answer: Basic Statistics is a subset of AI, which learns from experience. Machine learning is the application of statistical techniques to allow machines to improve with experience.
2.
What is 2. Different Sampling Techniques?
Answer: Types of Sampling Techniques Probability Sampling: Every member of the population has a known, non-zero chance of being selected. Simple Random Sampling: Each member of the population is selected randomly and independently, with equal probability. Stratified Random Sampling: The population is divided into strata based on certain criteria, then a simple random sample is taken from each stratum. Cluster Sampling: The population is divided into clusters or groups, then a random sample of clusters is selected. All members of the chosen clusters are included.
3.
Types of Probability?
Answer: Marginal Probability Marginal probability is the probability of an event occurring without considering the outcome of any other event. It is the probability of an individual event. Joint Probability Joint probability is the probability of two or more events occurring together. It is the probability of both events occurring simultaneously. Conditional Probability Conditional probability is the probability of an event occurring given that another event has occurred. It is the probability of an event after considering the outcome of a related event. Relation with Machine Learning Probability is an important concept in machine learning, as it provides a measure of the likelihood of an event occurring. Regression analysis, for example, involves predicting a continuous variable based on one or more independent variables, and the accuracy of this prediction is related to the probability of the dependent variable given the independent variables.
4.
what is Importance of Machine Learning?
Answer: Problem-solving with machine learning involves the following steps: identifying the problem, selecting a model, training the model, and testing the model. Implementing logistic regression using Python and scikit-learn is a common application. Probability and Statistics Probability is the measure of event likelihood. Measures of central tendency include mean, median, and mode. Measures of variability include range, interquartile range, variance, covariance, and standard deviation. Random experiment, sample space, and event are important concepts in probability. Probability Distributions Probability distributions include PDF, normal, and central limit theorem. Types of probability include marginal, joint, and conditional. Bayes theorem is the relation between conditional probabilities and inverse.
5.
Machine Learning Definitions?
Answer: Algorithm: A set of rules for learning patterns from data. Model: A representation of a machine learning process. Predictor Variable: A feature used to predict an outcome. Response Variable: The output feature being predicted. Probability: Measure of Event Likelihood Probability is a measure of the likelihood of an event occurring.
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