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 Hands-On Labs — nothing to install
- 14 Interactive Lessons and 88 topics mapped to the official exam objectives
Beginner Self-paced · 1 year access
52 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
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 Structured Lessons Comprehensive coverage of core course objectives
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52 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
14 Interactive Lessons · 88 topics01 Preface +
02 Introduction 7 topics · 2 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
03 Statistical Learning 4 topics · 3 LiveLab +
- What is Statistical Learning?
- Assessing Model Accuracy
- Lab: Introduction to Python
- Exercises
3 LiveLab in this lesson — see the labs panel →
04 Linear Regression 7 topics · 4 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
05 Classification 8 topics · 9 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
06 Resampling Methods 4 topics · 4 LiveLab +
- Cross-Validation
- The Bootstrap
- Lab: Cross-Validation and the Bootstrap
- Exercises
4 LiveLab in this lesson — see the labs panel →
07 Linear Model Selection and Regularization 6 topics · 6 LiveLab +
- Subnet Selection
- Shrinkage Methods
- Dimension Reduction Methods
- Considerations in High Dimensions
- Lab: Linear Models and Regularization Methods
- Exercises
6 LiveLab in this lesson — see the labs panel →
08 Moving Beyond Linearity 9 topics · 4 LiveLab +
- Polynomial Regression
- Step Functions
- Basis Functions
- Regression Splines
- Smoothing Splines
- Local Regression
- Generalized Additive Models
- Lab: Non-Linear Modeling
- Exercises
4 LiveLab in this lesson — see the labs panel →
09 Tree-Based Methods 4 topics · 4 LiveLab +
- The Basics of Decision Trees
- Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
- Lab: Tree-Based Methods
- Exercises
4 LiveLab in this lesson — see the labs panel →
10 Support Vector Machines 7 topics · 4 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
11 Deep Learning 10 topics · 2 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
12 Survival Analysis and Censored Data 9 topics · 3 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
13 Unsupervised Learning 6 topics · 3 LiveLab +
- The Challenge of Unsupervised Learning
- Principal Components Analysis
- Missing Values and Matrix Completion
- Clustering Methods
- Lab: Unsupervised Learning
- Exercises
3 LiveLab in this lesson — see the labs panel →
14 Multiple Testing 7 topics · 4 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
52 LiveLabs- 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 / FAQs
Questions before you start
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?+
Keep going
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Ready to Master Machine Learning in Python?
The future of data science belongs to those who understand the mechanics. Start your journey to becoming a lead developer and transform your team’s capabilities with this essential. Supervised Learning program.
- 1 year of full access
- 52 LiveLab included
- Certificate of completion
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