Grokking Machine Learning

Name: Grokking Machine Learning
Brand: Manning
Price: 39.43 EUR
Availability: OnlineOnly

Luis Serrano(Autor*in)

Manning (Verlag)

1. Auflage

Erschienen am 28. Dezember 2021

512 Seiten

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Beschreibung

Discover valuable machine learning techniques you can understand and apply using just high-school math.

In Grokking Machine Learning you will learn:

Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets

Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.

About the book
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

What's inside

Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets

About the reader
For readers who know basic Python. No machine learning knowledge necessary.

About the author
Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.

Table of Contents
1 What is machine learning? It is common sense, except done by a computer
2 Types of machine learning
3 Drawing a line close to our points: Linear regression
4 Optimizing the training process: Underfitting, overfitting, testing, and regularization
5 Using lines to split our points: The perceptron algorithm
6 A continuous approach to splitting points: Logistic classifiers
7 How do you measure classification models? Accuracy and its friends
8 Using probability to its maximum: The naive Bayes model
9 Splitting data by asking questions: Decision trees
10 Combining building blocks to gain more power: Neural networks
11 Finding boundaries with style: Support vector machines and the kernel method
12 Combining models to maximize results: Ensemble learning
13 Putting it all in practice: A real-life example of data engineering and machine learning

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Inhalt

Intro
Grokking Machine Learning
inside front cover
Copyright
contents
front matter
foreword
preface
acknowledgments
about this book
Types of chapters
Recommended learning paths
Appendices
Requirements and learning goals
Other resources
We'll be writing code
about 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 framework
Summary
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 data
Unsupervised learning: The branch of machine learning that works with unlabeled data
What is reinforcement learning?
Summary
Exercises
3 Drawing a line close to our points: Linear regression
The problem: We need to predict the price of a house
The solution: Building a regression model for housing prices
How to get the computer to draw this line: The linear regression algorithm
How do we measure our results? The error function
Real-life application: Using Turi Create to predict housing prices in India
What if the data is not in a line? Polynomial regression
Parameters and hyperparameters
Applications of regression
Summary
Exercises
4 Optimizing the training process: Underfitting, overfitting, testing, and regularization
An example of underfitting and overfitting using polynomial regression
How do we get the computer to pick the right model? By testing
Where did we break the golden rule, and how do we fix it? The validation set
A numerical way to decide how complex our model should be: The model complexity graph
Another alternative to avoiding overfitting: Regularization
Polynomial regression, testing, and regularization with Turi Create
Summary
Exercises
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 function
How to find a good classifier? The perceptron algorithm
Coding the perceptron algorithm
Applications of the perceptron algorithm
Summary
Exercises
6 A continuous approach to splitting points: Logistic classifiers
Logistic classifiers: A continuous version of perceptron classifiers
How to find a good logistic classifier? The logistic regression algorithm
Coding the logistic regression algorithm
Real-life application: Classifying IMDB reviews with Turi Create
Classifying into multiple classes: The softmax function
Summary
Exercises
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 them
A useful tool to evaluate our model: The receiver operating characteristic (ROC) curve
Summary
Exercises
8 Using probability to its maximum: The naive Bayes model
Sick or healthy? A story with Bayes' theorem as the hero
Use case: Spam-detection model
Building a spam-detection model with real data
Summary
Exercises
9 Splitting data by asking questions: Decision trees
The problem: We need to recommend apps to users according to what they are likely to download
The solution: Building an app-recommendation system
Beyond questions like yes/no
The graphical boundary of decision trees
Real-life application: Modeling student admissions with Scikit-Learn
Decision trees for regression
Applications
Summary
Exercises
10 Combining building blocks to gain more power: Neural networks
Neural networks with an example: A more complicated alien planet
Training neural networks
Coding neural networks in Keras
Neural networks for regression
Other architectures for more complex datasets
Summary
Exercises
11 Finding boundaries with style: Support vector machines and the kernel method
Using a new error function to build better classifiers
Coding support vector machines in Scikit-Learn
Training SVMs with nonlinear boundaries: The kernel method
Summary
Exercises
12 Combining models to maximize results: Ensemble learning
With a little help from our friends
Bagging: Joining some weak learners randomly to build a strong learner
AdaBoost: Joining weak learners in a clever way to build a strong learner
Gradient boosting: Using decision trees to build strong learners
XGBoost: An extreme way to do gradient boosting
Applications of ensemble methods
Summary
Exercises
13 Putting it all in practice: A real-life example of data engineering and machine learning
The Titanic dataset
Cleaning up our dataset: Missing values and how to deal with them
Feature engineering: Transforming the features in our dataset before training the models
Training our models
Tuning the hyperparameters to find the best model: Grid search
Using K-fold cross-validation to reuse our data as training and validation
Summary
Exercises
Appendix A. Solutions to the exercises
Chapter 2: Types of machine learning
Chapter 3: Drawing a line close to our points: Linear regression
Chapter 4: Optimizing the training process: Underfitting, overfitting, testing, and regularization
Chapter 5: Using lines to split our points: The perceptron algorithm
Chapter 6: A continuous approach to splitting points: Logistic classifiers
Chapter 7: How do you measure classification models? Accuracy and its friends
Chapter 8: Using probability to its maximum: The naive Bayes model
Chapter 9: Splitting data by asking questions: Decision trees
Chapter 10: Combining building blocks to gain more power: Neural networks
Chapter 11: Finding boundaries with style: Support vector machines and the kernel method
Chapter 12: Combining models to maximize results: Ensemble learning
Chapter 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 functions
Using gradient descent to train models
Using gradient descent for regularization
Getting stuck on local minima: How it happens, and how we solve it
Appendix C. References
General references
Courses
Blogs and YouTube channels
Books
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Graphics and image icons
index

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