STATS-PYTHON.AU1
An Introduction to Statistical Learning with Applications in Python
Transform your data science career by mastering statistical learning, the definitive skill set for the modern data professional.
- Practice in 52 Laboratorios prácticos — nothing to install
- 14 Lecciones interactivas y 88 topics mapped to the official exam objectives
Beginner A tu propio ritmo · 1 año de acceso
52 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / Habilidades que obtendrás
What you will be able to do
Are you ready to move beyond basic data manipulation and truly leverage machine learning in Python to revolutionize your decision-making process? The role of the data analyst is undergoing a fundamental shift, requiring specialized knowledge in how to strategically model complex systems. This ISLP course moves you past simple summary statistics and dives deep into the art and science of supervised learning and high-dimensional data analysis.
You will master the foundational mathematical frameworks, learn professional cross-validation techniques for model selection, and explore unsupervised learning to uncover hidden patterns in unlabeled data. Whether you are aiming for precise predictions using Linear Regression, building robust classifiers with support vector machines, or exploring the frontier of deep learning, this program provides the practical, hands-on knowledge to design and launch advanced models. From the bias-variance trade-off to modern resampling methods, you will learn to build systems that are both accurate and interpretable.
- Foundations & Linear Models: Master the core of statistical learning, building from basic matrix algebra to multiple linear regression and logistic regression for powerful predictive modeling.
- Resampling & Regularization: Tackle model accuracy through cross-validation and the bootstrap, while optimizing high-dimensional performance using ridge and lasso resampling methods.
- Tree-Based & Support Vector Machines: Move beyond simple linearity with Decision Trees, Random Forests, and Support Vector Machines to handle complex, non-linear datasets with precision.
- Deep & Unsupervised Learning: Explore the power of Neural Networks alongside Unsupervised Learning techniques like Clustering and PCA to find insights in data without predefined labels.
Course Highlights
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14 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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52 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
14 Lecciones interactivas · 88 topics01 Preface +
02 Introduction 7 topics · 2 Laboratorio en vivo +
- An Overview of Statistical Learning
- A Brief History of Statistical Learning
- This Course
- Who Should Read This Course?
- Notation and Simple Matrix Algebra
- Organization of This Course
- Data Sets Used in Labs and Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
03 Statistical Learning 4 topics · 3 Laboratorio en vivo +
- What is Statistical Learning?
- Assessing Model Accuracy
- Lab: Introduction to Python
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
04 Linear Regression 7 topics · 4 Laboratorio en vivo +
- Simple Linear Regression
- Multiple Linear Regression
- Other Considerations in the Regression Model
- The Marketing Plan
- Comparison of Linear Regression with K-Nearest Neighbors
- Lab: Linear Regression
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
05 Classification 8 topics · 9 Laboratorio en vivo +
- An Overview of Classification
- Why Not Linear Regression?
- Logistic Regression
- Generative Models for Classification
- A Comparison of Classification Methods
- Generalized Linear Models
- Lab: Logistic Regression, LDA, QDA, and KNN
- Exercises
9 Laboratorio en vivo in this lesson — see the labs panel →
06 Resampling Methods 4 topics · 4 Laboratorio en vivo +
- Cross-Validation
- The Bootstrap
- Lab: Cross-Validation and the Bootstrap
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Linear Model Selection and Regularization 6 topics · 6 Laboratorio en vivo +
- Subnet Selection
- Shrinkage Methods
- Dimension Reduction Methods
- Considerations in High Dimensions
- Lab: Linear Models and Regularization Methods
- Exercises
6 Laboratorio en vivo in this lesson — see the labs panel →
08 Moving Beyond Linearity 9 topics · 4 Laboratorio en vivo +
- Polynomial Regression
- Step Functions
- Basis Functions
- Regression Splines
- Smoothing Splines
- Local Regression
- Generalized Additive Models
- Lab: Non-Linear Modeling
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
09 Tree-Based Methods 4 topics · 4 Laboratorio en vivo +
- The Basics of Decision Trees
- Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
- Lab: Tree-Based Methods
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
10 Support Vector Machines 7 topics · 4 Laboratorio en vivo +
- Maximal Margin Classifier
- Support Vector Classifiers
- Support Vector Machines
- SVMs with More than Two Classes
- Relationship to Logistic Regression
- Lab: Support Vector Machines
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
11 Deep Learning 10 topics · 2 Laboratorio en vivo +
- Single Layer Neural Networks
- Multilayer Neural Networks
- Convolutional Neural Networks
- Document Classification
- Recurrent Neural Networks
- When to Use Deep Learning
- Fitting a Neural Network
- Interpolation and Double Descent
- Lab: Deep Learning
- Exercises
2 Laboratorio en vivo in this lesson — see the labs panel →
12 Survival Analysis and Censored Data 9 topics · 3 Laboratorio en vivo +
- Survival and Censoring Times
- A Closer Look at Censoring
- The Kaplan-Meier Survival Curve
- The Log-Rank Test
- Regression Models With a Survival Response
- Shrinkage for the Cox Model
- Additional Topics
- Lab: Survival Analysis
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
13 Unsupervised Learning 6 topics · 3 Laboratorio en vivo +
- The Challenge of Unsupervised Learning
- Principal Components Analysis
- Missing Values and Matrix Completion
- Clustering Methods
- Lab: Unsupervised Learning
- Exercises
3 Laboratorio en vivo in this lesson — see the labs panel →
14 Multiple Testing 7 topics · 4 Laboratorio en vivo +
- A Quick Review of Hypothesis Testing
- The Challenge of Multiple Testing
- The Family-Wise Error Rate
- The False Discovery Rate
- A Re-Sampling Approach to p-Values and False Discovery Rates
- Lab: Multiple Testing
- Exercises
4 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
52 Laboratorio en vivos- Analyzing the Wage Dataset
- Analyzing Stock Market Trends Using the Smarket Dataset
- Implementing the Bayes Classifier
- Implementing the Bias-Variance Trade-Off
- Indexing the Data
- Implementing Qualitative Predictors Using the Credit Dataset
- Implementing Non-Linear Transformations of Predictors
- Performing Multiple Linear Regression
- Implementing Simple Linear Regression
- Implementing Multiple Logistic Regression
- Implementing Multinomial Logistic Regression
- Generating and Visualizing a Multivariate Gaussian Distribution
- Implementing GLM
- Implementing Poisson Regression
- Implementing KNN on the Caravan Dataset
- Implementing Naive Bayes Classification
- Implementing QDA
- Implementing LDA
- Implementing LOOCV
- Implementing Bootstrapping Techniques on the Portfolio Dataset
- Implementing K-Fold Cross-Validation
- Implementing the Validation Set Approach
- Implementing Forward and Backward Stepwise Selection
- Improving Predictions with PCR
- Implementing PLS
- Implementing Lasso Regression
- Implementing Ridge Regression
- Implementing Subset Selection Methods Using the Hitters Dataset
- Implementing Splines
- Implementing a Step Function
- Improving GAM
- Implementing Polynomial Regression
- Building and Analyzing a Classification Tree Using the Carseats Dataset
- Improving Model Performance Using Boosting
- Implementing Bagging and Random Forests
- Fitting Regression Trees
- Implementing the Maximal Margin Classifier
- Creating and Analyzing an ROC Curve
- Implementing SVM with Multiple Classes
- Implementing SVC
- Implementing RNN for Time Series Prediction
- Creating an Image Classifier Using CNNs
- Implementing the Kaplan-Meier Survival Curve
- Applying the Log-Rank Test
- Incorporating Shrinkage Techniques into the Cox Model
- Implementing a Dendrogram
- Analyzing the NCI60 Dataset
- Implementing K-Means Clustering
- Implementing Holm's Step-Down Procedure
- Implementing the BH Procedure
- Implementing FDR
- Implementing FWER
03 / Preguntas frecuentes
Preguntas antes de empezar
Who should take the ISLP course?+
Does the course cover modern AI like Neural Networks and unsupervised learning?+
How much focus is there on Support Vector Machines?+
Is this course focused on theory or practical Machine Learning in Python?+
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