08 Oct What is Statistics

Statistics is a branch of mathematics and a vital tool in machine learning. It assists in understanding the underlying patterns in data. It collects, analyses, interprets, and presents data using statistical methods. Sir Ronald Aylmer Fisher is considered the father of modern statistics.

Statistics in machine learning is like the foundation or a core component that assists in making sense of data and decisions. Before moving further, let us quickly see how statistics play a major role in machine learning:

1. Descriptive Statistics:
   • Mean: The average value.
   • Median: The middle value.
   • Mode: The most frequently occurring value.
   • Variance and Standard Deviation: Measures of data spread and variability.
2. Inferential Statistics:
   • Hypothesis Testing: Making inferences or educated guesses about a population based on sample data.
   • Confidence Intervals: Range of values used to estimate a population parameter.
   • P-values: The probability of obtaining test results is at least as extreme as the results observed.
3. Probability Distributions:
   • Normal Distribution: A continuous probability distribution that is symmetrical around the mean.
   • Binomial Distribution: Discrete probability distribution of the number of successes in a sequence of independent experiments.
   • Poisson Distribution: Probability distribution that expresses the probability of a given number of events occurring in a fixed interval.
4. Regression Analysis:
   • Linear Regression: Modeling the relationship between a dependent variable and one or more independent variables.
   • Logistic Regression: Modeling the probability of a binary outcome based on one or more predictor variables.
5. Sampling:
   • Random Sampling: Each member of the population has an equal chance of being selected.
   • Stratified Sampling: The population is divided into subgroups, and samples are taken from each subgroup.
6. Bayesian Statistics:
   • Bayes’ Theorem: Describes the probability of an event based on prior knowledge of conditions related to the event.
   • Bayesian Inference: Updating the probability of a hypothesis as more evidence becomes available.

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Read More:

• What is Machine Learning
• What is a Machine Learning Model
• Types of Machine Learning
• Supervised vs Unsupervised vs Reinforcement Machine Learning
• What is Deep Learning
• Feedforward Neural Networks (FNN)
• Convolutional Neural Network (CNN)
• Recurrent Neural Networks (RNN)
• Long short-term memory (LSTM)
• Generative Adversarial Networks (GANs)