Enroll yourself in the Machine Learning Python course and lab to gain expertise on the processes, patterns, and strategies needed for building effective learning systems. The Machine learning course imparts skills that are required for understanding machine learning algorithms, models, and core machine learning concepts, evaluating classifiers and regressors, connections, extensions, and further directions. The study guide is equipped with learning resources to broaden your toolbox and explore some of the field’s most sophisticated and exciting techniques.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Enroll yourself in the Machine Learning Python course and lab to gain expertise on the processes, patterns, and strategies needed for building effective learning systems. The Machine learning course imparts skills that are required for understanding machine learning algorithms, models, and core machine learning concepts, evaluating classifiers and regressors, connections, extensions, and further directions. The study guide is equipped with learning resources to broaden your toolbox and explore some of the field’s most sophisticated and exciting techniques.

1

  • Let’s Discuss Learning

  • 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
2

  • Some Technical Background

  • 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
3

  • Predicting Categories: Getting Started with Classification

  • 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
4

  • Predicting Numerical Values: Getting Started with Regression

  • 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
5

  • Evaluating and Comparing Learners

  • 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
6

  • Evaluating Classifiers

  • 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
7

  • Evaluating Regressors

  • Baseline Regressors
    Additional Measures for Regression
    Residual Plots
    A First Look at Standardization
    Evaluating Regressors in a More Sophisticated Way: Take Two
    EOC
8

  • More Classification Methods

  • Revisiting Classification
    Decision Trees
    Support Vector Classifiers
    Logistic Regression
    Discriminant Analysis
    Assumptions, Biases, and Classifiers
    Comparison of Classifiers: Take Three
    EOC
9

  • More Regression Methods

  • Linear Regression in the Penalty Box: Regularization
    Support Vector Regression
    Piecewise Constant Regression
    Regression Trees
    Comparison of Regressors: Take Three
    EOC
10

  • Manual Feature Engineering: Manipulating Data for Fun and Profit

  • 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
11

  • Tuning Hyperparameters and Pipelines

  • Models, Parameters, Hyperparameters
    Tuning Hyperparameters
    Down the Recursive Rabbit Hole: Nested Cross-Validation
    Pipelines
    Pipelines and Tuning Together
    EOC
12

  • Combining Learners

  • Ensembles
    Voting Ensembles
    Bagging and Random Forests
    Boosting
    Comparing the Tree-Ensemble Methods
    EOC
13

  • Models That Engineer Features for Us

  • Feature Selection
    Feature Construction with Kernels
    Principal Components Analysis: An Unsupervised Technique
    EOC
14

  • Feature Engineering for Domains: Domain-Specific Learning

  • Working with Text
    Clustering
    Working with Images
    EOC
15

  • Connections, Extensions, and Further Directions

  • Optimization
    Linear Regression from Raw Materials
    Building Logistic Regression from Raw Materials
    SVM from Raw Materials
    Neural Networks
    Probabilistic Graphical Models
    EOC
16

  • Appendix A

  • mlwpy.py Listing

Audience

Experienced programmers will benefit.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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