Grokking Machine Learning

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. 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. 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 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 Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will 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, youll build interesting projects with Python, including models for spam detection and image recognition. Youll 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 Linear regression 4 Optimizing the training Underfitting, overfitting, testing, and regularization 5 Using lines to split our The perceptron algorithm 6 A continuous approach to splitting Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its The naive Bayes model 9 Splitting data by asking Decision trees 10 Combining building blocks to gain more Neural networks 11 Finding boundaries with Support vector machines and the kernel method 12 Combining models to maximize Ensemble learning 13 Putting it all in A real-life example of data engineering and machine learning It's Time To Dispel The Myth That Machine Learning Is Difficult. 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 Readily-available Machine Learning Tools! In Grokking Machine Learning, Expert Machine Learning Engineer Luis Serrano Introduces The Most Valuable Ml Techniques And Teaches You How To Make Them Work For You. Practical Examples Illustrate Each New Concept To Ensure You’re Grokking As You Go. You’ll Build Models For Spam Detection, Language Analysis, And Image Recognition As You Lock In Each Carefully-selected Skill. 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.