4 Hours of Video Instruction   Code-along sessions move you from introductory machine learning concepts to concrete code.   Overview   Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. These videos avoid heavy mathematics to focus on how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.   You will learn about the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You will learn techniques for getting the most informative learning performance measures out of your data. You will come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.   Skill Level   Beginner to Intermediate   Learn How To   Recognize underfitting and overfitting with graphical plots Make use of resampling techniques like cross-validation to get the most out of your data Graphically evaluate the learning performance of learning systems Compare production learners with baseline models over various classification metrics Build and evaluate confusion matrices and ROC curves Apply classification metrics to multiclass learning problems Develop precision-recall and lift curves for classifiers Compare production regression techniques with baseline regressors over various regression metrics Construct residual plots for regressors   Who Should Take This Course   This course is a good fit for anyone that needs to improve their fundamental understanding of machine learning concepts and become familiar with basic machine learning code. You might be a newer data scientist, a data analyst transitioning to the use of machine learning models, a research and development scientist looking to add machine learning techniques to your classical statistical training, or a manager adding data science/machine learning capabilities to your team.   Course Requirements Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also have familiarity with the vocabulary of machine learning (dataset, training set, test set, model), but knowledge about the concepts can be very shallow. They should have a working Python installation that allows you to use scikit-learn and matplotlib.   Lesson Descriptions   Lesson 1: Evaluating Learning Performance   Lesson 1 covers fundamental issues with learning systems and techniques to assess them. Lesson 1 starts with a discussion of error, cost, and complexity. Then, you learn about overfitting and underfitting: These happen when our model, data, and noise in the system interact with each other poorly. To identify these scenarios, we need to make clever use, and even reuse, of our data. We also look at general techniques to graphically view the performance of our model(s) and how they interact with the data.   Lesson 2: Evaluating Classifiers (Part 1)   Lessons 2 and 3 are about specific issues in evaluating classification systems. Lesson 2 begins with a general discussion of classification metrics, and then turns to baseline classifiers and metrics. Then, the focus is on the confusion matrix and metrics derived from it. The confusion matrix lays out the ways we are right and the ways we are wrong on an outcome-by-outcome basis. Here we focus on the case where we have two outcomes of interest.   Lesson 3: Evaluating Classifiers (Part 2)   Lesson 3 extends the discussion to include cases where we have more than two outcomes of interest. Several approaches to multiclass evaluation are discussed as well as some classification specific graphical techniques: cumulative response and lift curves. The lesson ends with a case study comparison of classifiers.   Lesson 4: Evaluating Regressors   Lesson 4 discusses techniques specific to evaluating regressors. The lesson begins with regression metrics and baseline regressors before turning to various regression metrics. It then covers how to develop custom, user-defined metrics. Next up are graphical evaluation techniques and followed by a quick look at pipelines and standardization. The lesson concludes with a case study comparing several different regression systems.   About Pearson Video Training   Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.   Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of Contents:
Lesson 1: Evaluating Learning Performance Topics 1.1       Error, Cost, and Complexity 1.2       Overfitting/Underfitting I: Synthetic Data 1.3       Overfitting/Underfitting II: Varying Model Complexity 1.4       Errors and Costs 1.5       Resampling Techniques 1.6       Cross-Validation 1.7       Leave-One-Out Cross-Validation 1.8       Stratification 1.9       Repeated Train-Test Splits 1.10    Graphical Techniques 1.11    Getting Graphical: Learning and Complexity Curves 1.12    Graphical Cross-Validation   Lesson 2: Evaluating Classifiers (Part 1) Topics 2.1       Classification Metrics 2.2       Baseline Classifiers and Metrics 2.3       The Confusion Matrix 2.4       Metrics from the Binary Confusion Matrix 2.5       Performance Curves 2.6       Understanding the ROC Curve and AUC 2.7       Comparing Classifiers with ROC and PR Curves   Lesson 3: Evaluating Classifiers (Part 2) Topics 3.1       Multi-Class Issues 3.2       Multi-class Metric Averages 3.3       Multi-class AUC: One-versus-Rest 3.4       Multi-class AUC: The Hand and Till Method 3.5       More Curves 3.6       Cumulative Response and Lift Curves 3.7       Case Study: A Classifier Comparison   Lesson 4: Evaluating Regressors Topics 4.1       Regression Metrics 4.2       Baseline Regressors 4.3       Regression Metrics: Custom Metrics and RMSE 4.4       Understanding the Default Regression Metric R^2 4.5       Errors and Residual Plots 4.6       Standardization 4.7       A Quick Pipeline and Standardization 4.8       Case Study: A Regressor Comparison