- Can you use word embedding to extract text features and redo the newsgroup classification project in Chapter 5, Classifying Newsgroup Topics with Support Vector Machines? (note that you might not be able to get better results with word embedding than tf-idf, but it is good practice.)
- Can you find several challenges in Kaggle (www.kaggle.com) and practice what you have learned throughout the entire book?
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Foreword
- Contributors
- Preface
- Section 1: Fundamentals of Machine Learning
- Getting Started with Machine Learning and Python
- Section 2: Practical Python Machine Learning By Example
- Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
- Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
- Detecting Spam Email with Naive Bayes
- Classifying Newsgroup Topics with Support Vector Machines
- Finding separating boundary with support vector machines
- Classifying newsgroup topics with SVMs
- More example – fetal state classification on cardiotocography
- A further example – breast cancer classification using SVM with TensorFlow
- Summary
- Exercise
- Predicting Online Ad Click-Through with Tree-Based Algorithms
- Brief overview of advertising click-through prediction
- Getting started with two types of data – numerical and categorical
- Exploring decision tree from root to leaves
- Implementing a decision tree from scratch
- Predicting ad click-through with decision tree
- Ensembling decision trees – random forest
- Summary
- Exercise
- Predicting Online Ad Click-Through with Logistic Regression
- Converting categorical features to numerical – one-hot encoding and ordinal encoding
- Classifying data with logistic regression
- Training a logistic regression model
- Training on large datasets with online learning
- Handling multiclass classification
- Implementing logistic regression using TensorFlow
- Feature selection using random forest
- Summary
- Exercises
- Scaling Up Prediction to Terabyte Click Logs
- Stock Price Prediction with Regression Algorithms
- Brief overview of the stock market and stock prices
- What is regression?
- Mining stock price data
- Estimating with linear regression
- Estimating with decision tree regression
- Estimating with support vector regression
- Estimating with neural networks
- Evaluating regression performance
- Predicting stock price with four regression algorithms
- Summary
- Exercise
- Section 3: Python Machine Learning Best Practices
- Machine Learning Best Practices
- Machine learning solution workflow
- Best practices in the data preparation stage
- Best practice 1 – completely understanding the project goal
- Best practice 2 – collecting all fields that are relevant
- Best practice 3 – maintaining the consistency of field values
- Best practice 4 – dealing with missing data
- Best practice 5 – storing large-scale data
- Best practices in the training sets generation stage
- Best practice 6 – identifying categorical features with numerical values
- Best practice 7 – deciding on whether or not to encode categorical features
- Best practice 8 – deciding on whether or not to select features, and if so, how to do so
- Best practice 9 – deciding on whether or not to reduce dimensionality, and if so, how to do so
- Best practice 10 – deciding on whether or not to rescale features
- Best practice 11 – performing feature engineering with domain expertise
- Best practice 12 – performing feature engineering without domain expertise
- Best practice 13 – documenting how each feature is generated
- Best practice 14 – extracting features from text data
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
- Exercises
- Other Books You May Enjoy
Exercises
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