Free Download Basic Statistics for AI Build the Foundation for ML
Published 10/2025
Created by Anvesha S
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 21 Lectures ( 54m ) | Size: 329 MB
Build a solid base in statistics to analyze data and power AI & Machine Learning models.
What you'll learn
Grasp the core concepts of statistics that form the backbone of Artificial Intelligence and Machine Learning.
Understand and apply measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
Interpret and analyze data using quartiles, percentiles, and interquartile range (IQR) to detect outliers.
Build intuition about probability, random variables, and real-world uncertainty.
Apply conditional probability and Bayes' theorem to everyday AI examples such as spam detection, recommendations, and risk prediction.
Use data visualizations (histograms, boxplots, scatter plots) to uncover patterns and relationships.
Connect every statistical concept directly to AI and ML workflows - from data cleaning and preprocessing to model evaluation.
Requirements
No prior statistics or programming knowledge required.
A basic curiosity about how AI and data work together.
A willingness to think analytically and follow step-by-step visual explanations.
Description
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.Gain insight into real-world AI case studies including student performance prediction, fraud detection, and Bayesian text classification. Who This Course Is ForThis course is ideal for:Beginners in data science, AI, or ML who want a clear and practical introduction to statistics.Students and professionals transitioning into AI or analytics roles.Software engineers who want to understand the math behind models they implement.Non-technical learners curious about how AI systems interpret and learn from data.No prior math or coding experience is required - every topic is explained step-by-step with real-world relevance. Why Take This CourseMost people jump into Machine Learning without understanding the "why" behind the algorithms.This course helps you build that foundation - the statistical intuition that separates a beginner from a true data professional.By the end of this course, you'll be able to:Confidently describe and summarize datasets.Apply probability concepts to AI problems.Interpret AI model outputs with statistical understanding.Lay a strong groundwork for advanced topics like Bayesian networks, regression, and deep learning.Modules Covered Module 1: Introduction to StatisticsWhy statistics matters in AITypes of data: numerical, categorical, ordinalPopulation vs sampleDescriptive vs inferential statisticsModule 2: Descriptive StatisticsMeasures of central tendency (mean, median, mode)Measures of dispersion (range, variance, standard deviation)Quartiles, percentiles, and IQRData visualization using histograms, boxplots, and scatter plotsModule 3: Probability BasicsRandom variables (discrete & continuous)Independent vs dependent eventsProbability rules, conditional probability, and Bayes' theoremAI examples: spam filtering, recommendation systems, risk analysis(More advanced modules on distributions, inferential statistics, regression, and AI case studies will be added soon!)Real-World Applications You'll ExploreHow Netflix and Amazon use probability to predict what you'll watch or buy nextHow spam filters classify messages using Bayes' TheoremHow banks use statistics to detect anomalies and potential fraudHow AI models use distributions to manage uncertainty in predictionsCourse OutcomeBy the end of this course, you'll:Have a strong statistical foundation for AI and Machine Learning.Be able to analyze, interpret, and visualize data effectively.Understand the math that drives every AI prediction.Be ready to move confidently into advanced ML topics and real-world data projects.No Memorization - Just Clear, Practical UnderstandingThis isn't a formula-cramming course.Every concept is explained visually and intuitively so you'll remember how and why things work - not just what the equation says. Enroll TodayStart your journey toward becoming an AI professional with a solid foundation in statistics.Once you understand how data behaves, you can teach machines to do the same.Enroll now and start speaking the language of AI - the language of statistics.
Who this course is for
Beginners in AI, ML, or Data Science who want to master the math that drives models.
Students and professionals in computer science, marketing, or analytics who need a clear, practical understanding of statistics.
Software engineers transitioning to data-driven roles and seeking to strengthen their analytical foundations.
Non-technical learners and entrepreneurs who want to understand AI reports, dashboards, and metrics with confidence.
Anyone curious about how machines make predictions, detect patterns, and reason with uncertainty.
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