Machine Learning in Python for Everyone (Video Collection)

Machine Learning in Python for Everyone (Video Collection)

by Mark Fenner
Released February 2023
Publisher(s): Pearson
ISBN: 0138092818

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Video description

Overview

12+ of Video Instruction
Machine Learning in Python for Everyone video collection is based on three video courses that teach everything about the foundations and tools for machine learning. As machine learning has moved from futuristic AI projects to data analysis on your desk, you need to begin to build models and start coding machine learning tasks.

This master class includes the following courses:

  • Machine Learning with Python for Everyone Part 1: Learning Foundations, 2nd Edition
  • Machine Learning with Python for Everyone, Part 2: Measuring Models
  • Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox

Machine Learning with Python for Everyone Part 1: Learning Foundations is code-along sessions moving you from introductory machine learning concepts to concrete code. These videos skew away from heavy mathematics and focus on using Python, scikit-learn. Our emphasis on stories, graphics and code builds your understanding of machine learning. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques.

Machine Learning with Python for Everyone, Part 2: Measuring Models teaches the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You learn techniques for getting the most informative learning performance measures out of your data. You come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.

Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox teaches about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You learn techniques for feature engineering, including scaling, discretization, and interactions. Finally, you tackle implementing pipelines for more complex processing and nested cross-validation for tuning hyperparameters.

About the Instructor

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

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, Prentice Hall, 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.

Table of contents

  1. Machine Learning with Python for Everyone, Part 1: Introduction
    1. Introduction
  2. Lesson 1: Software Background
    1. Topics
    2. 1.1 What Is Machine Learning?
    3. 1.2 Building Learning Systems
    4. 1.3 Environment Installation
    5. 1.4 Three Things You Can do with NumPy and matplotlib
    6. 1.5 Three Things You Can do with Pandas
    7. 1.6 Three Things You Can do with scikit-learn and Friends
    8. 1.7 Getting Help
  3. Lesson 2: Mathematical Background
    1. Topics
    2. 2.1 Probability
    3. 2.2 Distributions
    4. 2.3 Linear Combinations
    5. 2.4 Geometry, Part 1
    6. 2.5 Geometry, Part 2
    7. 2.6 Geometry, Part 3
    8. 2.7 When Computers and Math Meet
  4. Lesson 3: Beginning Classification (Part I)
    1. Topics
    2. 3.1 Setup and the Iris Dataset
    3. 3.2 Classification, Accuracy, and Splitting
    4. 3.3 Accuracy
    5. 3.4 Introduction to Nearest Neighbors and Naive Bayes
    6. 3.5 k-Nearest Neighbors
    7. 3.6 Train-Test Split and Nearest Neighbors (Part 1)
    8. 3.7 Train-Test Split and Nearest Neighbors (Part 2)
    9. 3.8 Naive Bayes
  5. Lesson 4: Beginning Classification (Part II)
    1. Topics
    2. 4.1 Learning Evaluation, Part 1
    3. 4.2 Learning Evaluation, Part 2
    4. 4.3 Resource Evaluation: Time
    5. 4.4 Resource Evaluation: Memory
    6. 4.5 Scripts
  6. Lesson 5: Beginning Regression (Part I)
    1. Topics
    2. 5.1 Setup and the Diabetes Dataset
    3. 5.2 Introducing Regression
    4. 5.3 Measures of Center
    5. 5.4 k-Nearest Neighbors for Regression
    6. 5.5 Introducing Linear Regression and NN Regression
    7. 5.6 Linear Regression, Part 1
    8. 5.7 Linear Regression, Part 2
  7. Lesson 6: Beginning Regression (Part II)
    1. Topics
    2. 6.1 Optimization, Part 1
    3. 6.2 Optimization, Part 2
    4. 6.3 Optimization, Part 3
    5. 6.4 Learning Performance
    6. 6.5 Resource Evaluation
  8. Summary
    1. Machine Learning with Python for Everyone, Part 1: Summary
  9. Machine Learning with Python for Everyone, Part 2: Introduction
    1. Introduction
  10. Lesson 1: Evaluating Learning Performance
    1. Topics
    2. 1.1 Error, Cost, and Complexity
    3. 1.2 Overfitting/Underfitting I: Synthetic Data
    4. 1.3 Overfitting/Underfitting II: Varying Model Complexity
    5. 1.4 Errors and Costs
    6. 1.5 Resampling Techniques
    7. 1.6 Cross-Validation
    8. 1.7 Leave-One-Out Cross-Validation
    9. 1.8 Stratification
    10. 1.9 Repeated Train-Test Splits
    11. 1.10 Graphical Techniques
    12. 1.11 Getting Graphical: Learning and Complexity Curves
    13. 1.12 Graphical Cross-Validation
  11. Lesson 2: Evaluating Classifiers (Part 1)
    1. Topics
    2. 2.1 Classification Metrics
    3. 2.2 Baseline Classifiers and Metrics
    4. 2.3 The Confusion Matrix
    5. 2.4 Metrics from the Binary Confusion Matrix
    6. 2.5 Performance Curves
    7. 2.6 Understanding the ROC Curve and AUC
    8. 2.7 Comparing Classifiers with ROC and PR Curves
  12. Lesson 3: Evaluating Classifiers (Part 2)
    1. Topics
    2. 3.1 Multi-Class Issues
    3. 3.2 Multi-class Metric Averages
    4. 3.3 Multi-class AUC: One-versus-Rest
    5. 3.4 Multi-class AUC: The Hand and Till Method
    6. 3.5 More Curves
    7. 3.6 Cumulative Response and Lift Curves
    8. 3.7 Case Study: A Classifier Comparison
  13. Lesson 4: Evaluating Regressors
    1. Topics
    2. 4.1 Regression Metrics
    3. 4.2 Baseline Regressors
    4. 4.3 Regression Metrics: Custom Metrics and RMSE
    5. 4.4 Understanding the Default Regression Metric R^2
    6. 4.5 Errors and Residual Plots
    7. 4.6 Standardization
    8. 4.7 A Quick Pipeline and Standardization
    9. 4.8 Case Study: A Regressor Comparison
  14. Summary
    1. Machine Learning with Python for Everyone, Part 2: Summary
  15. Machine Learning with Python for Everyone, Part 3: Introduction
    1. Introduction
  16. Lesson 1: Fundamental Classification Methods I
    1. Topics
    2. 1.1 Revisiting Classification
    3. 1.2 Decision Trees I
    4. 1.3 Decision Trees II
    5. 1.4 Support Vector Classifiers I
    6. 1.5 Support Vector Classifiers II
  17. Lesson 2: Fundamental Classification Methods II
    1. Topics
    2. 2.1 Logistic Regression I
    3. 2.2 Logistic Regression II
    4. 2.3 Discriminant Analysis I
    5. 2.4 Discriminant Analysis II
    6. 2.5 Bias and Variance of Classifiers
    7. 2.6 Comparing Classifiers
  18. Lesson 3: Fundamental Regression Methods
    1. Topics
    2. 3.1 Penalized Regression I
    3. 3.2 Penalized Regression II
    4. 3.3 Piecewise Constant Regression
    5. 3.4 Regression Trees
    6. 3.5 Bias and Variance of Regressors
    7. 3.6 Comparing Regressors
  19. Lesson 4: Manual Feature Engineering
    1. Topics
    2. 4.1 Overview of Feature Engineering
    3. 4.2 Feature Scaling
    4. 4.3 Discretization
    5. 4.4 Categorical Coding
    6. 4.5 Interactions
    7. 4.6 Target Manipulations
  20. Lesson 5: Hyperparameters and Pipelines
    1. Topics
    2. 5.1 Models, Parameters, and Hyperparameters
    3. 5.2 Tuning Hyperparameters
    4. 5.3 Nested Cross-validation
    5. 5.4 Pipelines
    6. 5.5 Tuning Pipelines
  21. Summary
    1. Machine Learning with Python for Everyone, Part 3: Summary

Product information

  • Title: Machine Learning in Python for Everyone (Video Collection)
  • Author(s): Mark Fenner
  • Release date: February 2023
  • Publisher(s): Pearson
  • ISBN: 0138092818