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ML-PYTHON.AP1.0L0
Machine Learning with Python Lab
Intermediate Self-paced · 1 year access
35 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
16 Interactive Lessons · 105 topics01 Let’s Discuss Learning 8 topics +
- Welcome
- Scope, Terminology, Prediction, and Data
- Putting the Machine in Machine Learning
- Examples of Learning Systems
- Evaluating Learning Systems
- A Process for Building Learning Systems
- Assumptions and Reality of Learning
- End-of-Lesson Material
02 Some Technical Background 11 topics · 7 LiveLab +
- About Our Setup
- The Need for Mathematical Language
- Our Software for Tackling Machine Learning
- Probability
- Linear Combinations, Weighted Sums, and Dot Products
- A Geometric View: Points in Space
- Notation and the Plus-One Trick
- Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
- NumPy versus “All the Maths”
- Floating-Point Issues
- EOC
7 LiveLab in this lesson — see the labs panel →
03 Predicting Categories: Getting Started with Classification 8 topics · 1 LiveLab +
- Classification Tasks
- A Simple Classification Dataset
- Training and Testing: Don’t Teach to the Test
- Evaluation: Grading the Exam
- Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
- Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
- Simplistic Evaluation of Classifiers
- EOC
1 LiveLab in this lesson — see the labs panel →
04 Predicting Numerical Values: Getting Started with Regression 6 topics · 3 LiveLab +
- A Simple Regression Dataset
- Nearest-Neighbors Regression and Summary Statistics
- Linear Regression and Errors
- Optimization: Picking the Best Answer
- Simple Evaluation and Comparison of Regressors
- EOC
3 LiveLab in this lesson — see the labs panel →
05 Evaluating and Comparing Learners 9 topics · 3 LiveLab +
- Evaluation and Why Less Is More
- Terminology for Learning Phases
- Major Tom, There’s Something Wrong: Overfitting and Underfitting
- From Errors to Costs
- (Re)Sampling: Making More from Less
- Break-It-Down: Deconstructing Error into Bias and Variance
- Graphical Evaluation and Comparison
- Comparing Learners with Cross-Validation
- EOC
3 LiveLab in this lesson — see the labs panel →
06 Evaluating Classifiers 8 topics · 4 LiveLab +
- Baseline Classifiers
- Beyond Accuracy: Metrics for Classification
- ROC Curves
- Another Take on Multiclass: One-versus-One
- Precision-Recall Curves
- Cumulative Response and Lift Curves
- More Sophisticated Evaluation of Classifiers: Take Two
- EOC
4 LiveLab in this lesson — see the labs panel →
07 Evaluating Regressors 6 topics · 3 LiveLab +
- Baseline Regressors
- Additional Measures for Regression
- Residual Plots
- A First Look at Standardization
- Evaluating Regressors in a More Sophisticated Way: Take Two
- EOC
3 LiveLab in this lesson — see the labs panel →
08 More Classification Methods 8 topics · 3 LiveLab +
- Revisiting Classification
- Decision Trees
- Support Vector Classifiers
- Logistic Regression
- Discriminant Analysis
- Assumptions, Biases, and Classifiers
- Comparison of Classifiers: Take Three
- EOC
3 LiveLab in this lesson — see the labs panel →
09 More Regression Methods 6 topics · 2 LiveLab +
- Linear Regression in the Penalty Box: Regularization
- Support Vector Regression
- Piecewise Constant Regression
- Regression Trees
- Comparison of Regressors: Take Three
- EOC
2 LiveLab in this lesson — see the labs panel →
10 Manual Feature Engineering: Manipulating Data for Fun and Profit 8 topics · 2 LiveLab +
- Feature Engineering Terminology and Motivation
- Feature Selection and Data Reduction: Taking out the Trash
- Feature Scaling
- Discretization
- Categorical Coding
- Relationships and Interactions
- Target Manipulations
- EOC
2 LiveLab in this lesson — see the labs panel →
11 Tuning Hyperparameters and Pipelines 6 topics +
- Models, Parameters, Hyperparameters
- Tuning Hyperparameters
- Down the Recursive Rabbit Hole: Nested Cross-Validation
- Pipelines
- Pipelines and Tuning Together
- EOC
12 Combining Learners 6 topics · 1 LiveLab +
- Ensembles
- Voting Ensembles
- Bagging and Random Forests
- Boosting
- Comparing the Tree-Ensemble Methods
- EOC
1 LiveLab in this lesson — see the labs panel →
13 Models That Engineer Features for Us 4 topics · 4 LiveLab +
- Feature Selection
- Feature Construction with Kernels
- Principal Components Analysis: An Unsupervised Technique
- EOC
4 LiveLab in this lesson — see the labs panel →
14 Feature Engineering for Domains: Domain-Specific Learning 4 topics · 1 LiveLab +
- Working with Text
- Clustering
- Working with Images
- EOC
1 LiveLab in this lesson — see the labs panel →
15 Connections, Extensions, and Further Directions 7 topics · 1 LiveLab +
- Optimization
- Linear Regression from Raw Materials
- Building Logistic Regression from Raw Materials
- SVM from Raw Materials
- Neural Networks
- Probabilistic Graphical Models
- EOC
1 LiveLab in this lesson — see the labs panel →
16 Appendix A: mlwpy.py Listing +
Hands-On Labs Our edge
35 LiveLabs- Plotting a Probability Distribution Graph
- Using the zip Function
- Calculating the Sum of Squares
- Plotting a Line Graph
- Plotting a 3D Graph
- Plotting a Polynomial Graph
- Using the numpy.dot() Method
- Displaying Histograms
- Defining an Outlier
- Calculating the Median Value
- Estimating the Multiple Regression Equation
- Constructing a Swarm Plot
- Using the describe() Method
- Viewing Variance
- Creating a Confusion Matrix
- Creating an ROC Curve
- Recreating an ROC Curve
- Creating a Trendline Graph
- Viewing the Standard Deviation
- Constructing a Scatterplot
- Evaluating the Prediction Error Rates
- Evaluating a Logistic Model
- Creating a Covariance Matrix
- Using the load_digits() Function
- Illustrating a Less Consistent Relationship
- Illustrating a Piecewise Constant Regression
- Manipulating the Target
- Manipulating the Input Space
- Calculating the Mean Value
- Displaying a Correlation Matrix
- Creating a Nonlinear Model
- Performing a Principal Component Analysis
- Using the Manifold Method
- Encoding Text
- Building an Estimated Simple Linear Regression Equation
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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