PS-ML.AU1
Probability and Statistics for Machine Learning
Start your career with the Probability & Statistics for Machine Learning course. Learn how to design, evaluate, and understand the next generation of AI models.
- Practice in 30 Laboratorios prácticos — nothing to install
- 12 Lecciones interactivas y 103 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
30 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
12Lecciones interactivas
103Topics
30Laboratorio en vivo
110Tarjetas didácticas
110Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Are you tired of treating machine learning models like black boxes? This statistics course gives you the rigorous foundation to simply build, evaluate & troubleshoot AI algorithms. Therefore, the power of modern data science & AI lies in the mathematical principles—especially probability & statistics for machine learning.
Mastering statistics for machine learning is an important differentiator for securing high-end roles in the fields of data science & AI engineering. For anyone aiming to master AI, this is the definition of math for the Machine Learning Program. By understanding the probability for data science, it is no longer optional—it is optional for anyone who is opting for a career in statistics for AI.
- Core Probability & Data Analysis: Dive into the essentials of probability, random variables, expected value & common distributions—the statistical backbone for all the machine learning models. You can deepen the probability for data science expertise.
- Statistical Inference & Testing: Master hypothesis testing, confidence intervals, and ANOVA, as well as the central limit theorem for rigorous model validation, a key skill in statistics for AI.
- Model Building Blocks: Learn the maximum likelihood estimation, the bias-variance trade-off & how to reconstruct common distributions from data, essential for practical statistics for machine learning.
- Probabilistic Algorithms: Understand the math behind models such as regression, classification, and unsupervised learning, as well as Markov.
Course Highlights
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12 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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30 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
12 Lecciones interactivas · 103 topics01 Preface 2 topics +
- Prerequisites for the Book
- Notations
02 Probability and Statistics: An Introduction 8 topics · 3 Laboratorio en vivo +
- Introduction
- Representing Data
- Summarizing and Visualizing Data
- The Basics of Probability and Probability Distributions
- Hypothesis Testing
- Basic Problems in Machine Learning
- Summary
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
03 Summarizing and Visualizing Data 6 topics · 5 Laboratorio en vivo +
- Introduction
- Summarizing Data
- Data Visualization
- Applications to Data Preprocessing
- Summary
- Exercises
5 Laboratorio en vivo in this lesson — see the labs panel →
04 Probability Basics and Random Variables 13 topics · 3 Laboratorio en vivo +
- Introduction
- Sample Spaces and Events
- The Counting Approach to Probabilities
- Set-Wise View of Events
- Conditional Probabilities and Independence
- The Bayes Rule
- The Basics of Probability Distributions
- Distribution Independence and Conditionals
- Summarizing Distributions
- Compound Distributions
- Functions of Random Variables (*)
- Summary
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Probability Distributions 16 topics · 2 Laboratorio en vivo +
- Introduction
- The Uniform Distribution
- The Bernoulli Distribution
- The Categorical Distribution
- The Geometric Distribution
- The Binomial Distribution
- The Multinomial Distribution
- The Exponential Distribution
- The Poisson Distribution
- The Normal Distribution
- The Student’s t-Distribution
- The χ2-Distribution
- Mixture Distributions: The Realistic View
- Moments of Random Variables (*)
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
06 Hypothesis Testing and Confidence Interval 10 topics · 4 Laboratorio en vivo +
- Introduction
- The Central Limit Theorem
- Sampling Distribution and Standard Error
- The Basics of Hypothesis Testing
- Hypothesis Tests For Differences in Means
- χ2-Hypothesis Tests
- Analysis of Variance (ANOVA)
- Machine Learning Applications of Hypothesis Testing
- Summary
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Reconstructing Probability Distributions from Data 10 topics · 2 Laboratorio en vivo +
- Introduction
- Maximum Likelihood Estimation
- Reconstructing Common Distributions from Data
- Mixture of Distributions: The EM Algorithm
- Kernel Density Estimation
- Reducing Reconstruction Variance
- The Bias-Variance Trade-Off
- Popular Distributions Used as Conjugate Priors (*)
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
08 Regression 11 topics · 3 Laboratorio en vivo +
- Introduction
- The Basics of Regression
- Two Perspectives on Linear Regression
- Solutions to Linear Regression
- Handling Categorical Predictors
- Overfitting and Regularization
- A Probabilistic View of Regularization
- Evaluating Linear Regression
- Nonlinear Regression
- Summary
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
09 Classification: A Probabilistic View 6 topics · 2 Laboratorio en vivo +
- Introduction
- Generative Probabilistic Models
- Loss-Based Formulations: A Probabilistic View
- Beyond Classification: Ordered Logit Model
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
10 Unsupervised Learning: A Probabilistic View 6 topics · 3 Laboratorio en vivo +
- Introduction
- Mixture Models for Clustering
- Matrix Factorization
- Outlier Detection
- Summary
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
11 Discrete State Markov Processes 8 topics · 1 Laboratorio en vivo +
- Introduction
- Markov Chains
- Machine Learning Applications of Markov Chains
- Markov Chains to Generative Models
- Hidden Markov Models
- Applications of Hidden Markov Models
- Summary
- Exercises
1 Laboratorio en vivo in this lesson — see the labs panel →
12 Probabilistic Inequalities and Approximations 7 topics · 2 Laboratorio en vivo +
- Introduction
- Jensen’s Inequality
- Markov and Chebyshev Inequalities
- Approximations for Sums of Random Variables
- Tail Inequalities Versus Approximation Estimates
- Summary
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
30 Laboratorio en vivos- Preparing Data for Regression and Visualization
- Performing Hypothesis Testing
- Modeling Sensor Noise in Robotics
- Analyzing Data Using Bar Charts
- Analyzing Data Using Scatter Plots and Line Plots
- Analyzing Data Using Histograms
- Building a Multivariate Model
- Building a Univariate Model
- Implementing the Bayes Classifier
- Training a Naïve Bayes Model
- Creating a Naïve Bayes Spam Classifier
- Generating a Binomial Distribution Plot
- Generating and Visualizing a Gaussian Distribution
- Using Sampling to Convert Bimodal Data to a Normal Distribution
- Evaluating AI Model Accuracy with Statistical Tests
- Testing Hypotheses: Type I and II Errors
- Calculating and Interpreting Confidence Intervals
- Implementing the Bias-Variance Trade-Off
- Working with Conjugate Priors and Estimating Parameters
- Training a Linear Regression Model
- Implementing Lasso Regression
- Implementing Non-Linear Transformations of Predictors
- Implementing Multinomial Logistic Regression
- Training a Logistic Regression Model
- Implementing the Squared Loss Model
- Implementing PLSA
- Detecting Outliers Using the Mahalanobis Distance Method
- Using an HMM Model
- Applying Markov and Chebyshev Inequalities
- Applying Chernoff Bounds and Hoeffding Inequalities
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Who should take the Probability & Statistics for Machine Learning course?+
Data science professionals, machine learning engineers, & anyone who wants to gain a deep understanding of the statistical mathematics underlying AI algorithms, such as neural networks and generative models.
What are the key takeaways from the Probability & Statistics for Machine Learning course?+
You will master essential topics such as maximum likelihood estimation, hypothesis testing, & probabilistic models for regression & classification, which are important for robust statistics in machine learning.
Does the course cover practical application?+
Absolutely. It covers both the theory & hands-on implementation of concepts like Bayesian methods, Gaussian distributions, and hypothesis testing using data, which makes it perfect for a data science program.
How is this course different from a general statistics class?+
This program is specially designed for AI, directly linking core concepts such as Bias-variance trade-off and Markov processes to modern machine learning techniques, offering you a distinct advantage in statistics for AI. Your true math for machine learning mastery starts here.
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- 1 año de acceso completo
- 30 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito