book

Grokking Machine Learning

Name: Grokking Machine Learning
Author: Luis Serrano
ISBN: 9781617295911

by Luis Serrano

December 2021

Intermediate to advanced

512 pages

15h 23m

English

Manning Publications

Audiobook available

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Grokking Machine Learning
inside front cover
Copyright
contents
front matter
forewordprefaceacknowledgmentsabout this bookTypes of chaptersRecommended learning pathsAppendicesRequirements and learning goalsOther resourcesWe’ll be writing codeabout the author
1 What is machine learning? It is common sense, except done by a computer
Do I need a heavy math and coding background to understand machine learning?OK, so what exactly is machine learning?How do we get machines to make decisions with data? The remember-formulate-predict frameworkSummary
2 Types of machine learning
What is the difference between labeled and unlabeled data?Supervised learning: The branch of machine learning that works with labeled dataUnsupervised learning: The branch of machine learning that works with unlabeled dataWhat is reinforcement learning?SummaryExercises
3 Drawing a line close to our points: Linear regression
The problem: We need to predict the price of a houseThe solution: Building a regression model for housing pricesHow to get the computer to draw this line: The linear regression algorithmHow do we measure our results? The error functionReal-life application: Using Turi Create to predict housing prices in IndiaWhat if the data is not in a line? Polynomial regressionParameters and hyperparametersApplications of regressionSummaryExercises
4 Optimizing the training process: Underfitting, overfitting, testing, and regularization
An example of underfitting and overfitting using polynomial regressionHow do we get the computer to pick the right model? By testingWhere did we break the golden rule, and how do we fix it? The validation setA numerical way to decide how complex our model should be: The model complexity graphAnother alternative to avoiding overfitting: RegularizationPolynomial regression, testing, and regularization with Turi CreateSummaryExercises
5 Using lines to split our points: The perceptron algorithm
The problem: We are on an alien planet, and we don’t know their language!How do we determine whether a classifier is good or bad? The error functionHow to find a good classifier? The perceptron algorithmCoding the perceptron algorithmApplications of the perceptron algorithmSummaryExercises

6 A continuous approach to splitting points: Logistic classifiers
Logistic classifiers: A continuous version of perceptron classifiersHow to find a good logistic classifier? The logistic regression algorithmCoding the logistic regression algorithmReal-life application: Classifying IMDB reviews with Turi CreateClassifying into multiple classes: The softmax functionSummaryExercises
7 How do you measure classification models? Accuracy and its friends
Accuracy: How often is my model correct?How to fix the accuracy problem? Defining different types of errors and how to measure themA useful tool to evaluate our model: The receiver operating characteristic (ROC) curveSummaryExercises
8 Using probability to its maximum: The naive Bayes model
Sick or healthy? A story with Bayes’ theorem as the heroUse case: Spam-detection modelBuilding a spam-detection model with real dataSummaryExercises
9 Splitting data by asking questions: Decision trees
The problem: We need to recommend apps to users according to what they are likely to downloadThe solution: Building an app-recommendation systemBeyond questions like yes/noThe graphical boundary of decision treesReal-life application: Modeling student admissions with Scikit-LearnDecision trees for regressionApplicationsSummaryExercises
10 Combining building blocks to gain more power: Neural networks
Neural networks with an example: A more complicated alien planetTraining neural networksCoding neural networks in KerasNeural networks for regressionOther architectures for more complex datasetsSummaryExercises
11 Finding boundaries with style: Support vector machines and the kernel method
Using a new error function to build better classifiersCoding support vector machines in Scikit-LearnTraining SVMs with nonlinear boundaries: The kernel methodSummaryExercises
12 Combining models to maximize results: Ensemble learning
With a little help from our friendsBagging: Joining some weak learners randomly to build a strong learnerAdaBoost: Joining weak learners in a clever way to build a strong learnerGradient boosting: Using decision trees to build strong learnersXGBoost: An extreme way to do gradient boostingApplications of ensemble methodsSummaryExercises
13 Putting it all in practice: A real-life example of data engineering and machine learning
The Titanic datasetCleaning up our dataset: Missing values and how to deal with themFeature engineering: Transforming the features in our dataset before training the modelsTraining our modelsTuning the hyperparameters to find the best model: Grid searchUsing K-fold cross-validation to reuse our data as training and validationSummaryExercises
Appendix A. Solutions to the exercises
Chapter 2: Types of machine learningChapter 3: Drawing a line close to our points: Linear regressionChapter 4: Optimizing the training process: Underfitting, overfitting, testing, and regularizationChapter 5: Using lines to split our points: The perceptron algorithmChapter 6: A continuous approach to splitting points: Logistic classifiersChapter 7: How do you measure classification models? Accuracy and its friendsChapter 8: Using probability to its maximum: The naive Bayes modelChapter 9: Splitting data by asking questions: Decision treesChapter 10: Combining building blocks to gain more power: Neural networksChapter 11: Finding boundaries with style: Support vector machines and the kernel methodChapter 12: Combining models to maximize results: Ensemble learningChapter 13: Putting it all in practice: A real-life example of data engineering and machine learning
Appendix B. The math behind gradient descent: Coming down a mountain using derivatives and slopes
Using gradient descent to decrease functionsUsing gradient descent to train modelsUsing gradient descent for regularizationGetting stuck on local minima: How it happens, and how we solve it
Appendix C. References
General referencesCoursesBlogs and YouTube channelsBooksChapter 1Chapter 2Chapter 3Chapter 4Chapter 5Chapter 6Chapter 7Chapter 8Chapter 9Chapter 10Chapter 11Chapter 12Chapter 13Graphics and image icons
index

Content preview from Grokking Machine Learning

Appendix B. The math behind gradient descent: Coming down a mountain using derivatives and slopes

In this appendix, we’ll go over the mathematical details of gradient descent. This appendix is fairly technical, and understanding it is not required to follow the rest of the book. However, it is here to provide a sense of completeness for the readers who wish to understand the inner workings of some of the core machine learning algorithms. The mathematics knowledge required for this appendix is higher than for the rest of the book. More specifically, knowledge of vectors, derivatives, and the chain rule is required.

In chapters 3, 5, 6, 10, and 11, we used gradient descent to minimize the error functions in our models. More specifically, we used ...

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Grokking Machine Learning

by Luis Serrano

Appendix B. The math behind gradient descent: Coming down a mountain using derivatives and slopes

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