Udemy MOOC / Non-credit 0

Basic Statistics for AI: Build the Foundation for ML

Name: Basic Statistics for AI: Build the Foundation for ML
Price: 109.99 USD
Availability: InStock

About this course

Statistics is the language of data — and data is the foundation of every Artificial Intelligence (AI) and Machine Learning (ML) system.If you’ve ever wondered how models make predictions, detect anomalies, or recommend products, it all starts with statistics.This course — Basic Statistics for AI: Build the Foundation for Machine Learning — is designed to give you a complete understanding of the math and statistics concepts that drive AI models, even if you’re starting from scratch.You’ll learn not just formulas, but also why each concept matters and how it connects to real-world AI applications like spam detection, recommendation systems, and predictive modeling. What You’ll LearnUnderstand why statistics is essential for AI and ML, and how it powers data-driven decision-making.Identify and analyze different types of data — numerical, categorical, and ordinal.Differentiate between population and sample and learn how sampling impacts AI modeling.Master descriptive statistics — mean, median, mode, variance, standard deviation, quartiles, and percentiles.Learn how to visualize data using histograms, box plots, and scatter plots to uncover patterns and outliers.Build a strong foundation in probability theory — understand random variables, independence, dependence, and conditional probability.Apply Bayes’ theorem to real AI problems like spam detection and recommendations.Discover how probability distributions like binomial, Poisson, and normal distributions explain real-world AI events.Explore the Central Limit Theorem and how it enables statistical inference in large datasets.

62/100

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What the provider tells you: 38/45
Who stands behind it: 8/35
How complete the listing is: 16/20

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What you'll learn

Understand the essential role of statistics in AI and ML
Identify different types of data and their characteristics
Differentiate between populations and samples
Master descriptive statistics including mean, median, and standard deviation
Visualize data using various graphical tools
Apply fundamental concepts of probability theory
Use Bayes' theorem in practical scenarios
Explore probability distributions and their implications
Understand the Central Limit Theorem in context

Machine Learning Statistics & Probability #data-driven decision making #machine learning #data visualization #descriptive statistics #data types #data analysis #statistics #probability #central limit theorem #sampling #bayes theorem

$109.99

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