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Overview
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
Product Details
| ISBN-13: | 9781617295911 |
|---|---|
| Publisher: | Manning |
| Publication date: | 12/14/2021 |
| Pages: | 512 |
| Sales rank: | 689,541 |
| Product dimensions: | 7.38(w) x 9.25(h) x 1.30(d) |
About the Author
Table of Contents
Foreword ix
Preface xi
Acknowledgments xiii
About this book xv
About the author xix
1 What is machine learning? It is common sense, except done by a computer 1
Do I need a heavy math and coding background to understand machine learning? 2
OK, so what exactly is machine learning? 3
How do we get machines to make decisions with data? The remember-formulate-predict framework 6
2 Types of machine learning 15
What is the difference between labeled and unlabeled data? 17
Supervised learning: The branch of machine learning that works with labeled data 18
Unsupervised learning: The branch of machine learning that works with unlabeled data 22
What is reinforcement learning? 29
3 Drawing a line close to our points: Linear regression 35
The problem: We need to predict the price of a house 37
The solution: Building a regression model for housing prices 38
How to get the computer to draw this Sine: The linear regression algorithm 44
How do we measure our results? The error function 60
Real-life application: Using Turi Create to predict housing prices in India 67
What if the data is not in a line? Polynomial regression 69
Parameters and hyperparameters 71
Applications of regression 72
4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 77
An example of underfitting and overfitting using polynomial regression 79
How do we get the computer to pick the right model? By testing 81
Where did we break the golden rule, and how do we fix it? The validation set 84
A numerical way to decide how complex our model should be: The model complexity graph 85
Another alternative to avoiding overfitting: Regularization 86
Polynomial regression, testing, and regularization with Turi Create 95
5 Using lines to split our points: The perceptron algorithm 103
The problem: We are on an alien planet, and we don't know their language! 106
How do we determine whether a classifier is good or bad? The error function 121
How to find a good classifier? The perceptron algorithm 129
Coding the perceptron algorithm 137
Applications of the perceptron algorithm 142
6 A continuous approach to splitting points: Logistic classifiers 147
Logistic classifiers: A continuous version of perceptron classifiers 149
How to find a good logistic classifier? The logistic regression algorithm 160
Coding the logistic regression algorithm 166
Real-life application: Classifying IMDB reviews with Turi Create 171
Classifying into multiple classes: The softmax function 173
7 How do you measure classification models? Accuracy and its friends 177
Accuracy: How often is my model correct? 178
How to fix the accuracy problem? Defining different types of errors and how to measure them 179
A useful tool to evaluate our model: The receiver operating characteristic (ROC) curve 189
8 Using probability to its maximum: The naive Bayes model 205
Sick or healthy? A story with Bayes' theorem as the hero 207
Use case: Spam-detection model 212
Building a spam-detection model with real data 226
9 Splitting data by asking questions: Decision trees 233
The problem: We need to recommend apps to users according to what they are likely to download 240
The solution: Building an app-recommendation system 241
Beyond questions like yes/no 257
The graphical boundary of decision trees 261
Real-life application: Modeling student admissions with Scikit-Learn 264
Decision trees for regression 268
Applications 272
10 Combining building blocks to gain more power: Neural networks 277
Neural networks with an example: A more complicated alien planet 279
Training neural networks 292
Coding neural networks in Keras 299
Neural networks for regression 308
Other architectures for more complex datasets 309
11 Finding boundaries with style: Support vector machines and the kernel method 315
Using a new error function to build better classifiers 318
Coding support vector machines in Scikit-Learn 324
Training SVMs with nonlinear boundaries: The kernel method 326
12 Combining models to maximize results: Ensemble learning 351
With a little help from our friends 352
Bagging: Joining some weak learners randomly to build a strong learner 354
AdaBoost: Joining weak learners in a clever way to build a strong learner 360
Gradient boosting: Using decision trees to build strong learners 370
XGBoost: An extreme way to do gradient boosting 375
Applications of ensemble methods 384
13 Putting it all in practice: A real-life example of data engineering and machine learning 387
The Titanic dataset 388
Cleaning up our dataset: Missing values and how to deal with them 392
Feature engineering: Transforming the features in our dataset before training the models 395
Training our models 400
Tuning the hyperparameters to find the best model: Grid search 405
Using K-fold cross-validation to reuse our data as training and validation 408
Solutions to the exercises 411
The math behind gradient descent: Coming down a mountain using derivatives and slopes 449
References 471
Index 481