Python Machine Learning By Example.: Implement machine learning algorithms and techniques to build intelligent systems, Second Edition

Section 2: Practical Python Machine Learning By Example

3. Section 2: Practical Python Machine Learning By Example

4. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques

Exploring the 20 Newsgroups Dataset with Text Analysis Techniques

How computers understand language - NLP

Picking up NLP basics while touring popular NLP libraries

Getting the newsgroups data

Exploring the newsgroups data

Thinking about features for text data

Visualizing the newsgroups data with t-SNE

Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

5. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

Learning without guidance – unsupervised learning

Clustering newsgroups data using k-means

Discovering underlying topics in newsgroups

Topic modeling using NMF

Topic modeling using LDA

Detecting Spam Email with Naive Bayes

6. Detecting Spam Email with Naive Bayes

Getting started with classification

Exploring Naïve Bayes

Classification performance evaluation

Model tuning and cross-validation

Classifying Newsgroup Topics with Support Vector Machines

7. 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

Predicting Online Ad Click-Through with Tree-Based Algorithms

8. 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

Predicting Online Ad Click-Through with Logistic Regression

9. 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

Scaling Up Prediction to Terabyte Click Logs

10. Scaling Up Prediction to Terabyte Click Logs

Learning the essentials of Apache Spark

Programming in PySpark

Learning on massive click logs with Spark

Feature engineering on categorical variables with Spark

Stock Price Prediction with Regression Algorithms

11. 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

Section 3: Python Machine Learning Best Practices

12. Section 3: Python Machine Learning Best Practices

13. Machine Learning Best Practices

Machine Learning Best Practices

Machine learning solution workflow

Best practices in the data preparation stage

Best practices in the training sets generation stage

Best practices in the model training, evaluation, and selection stage

Best practices in the deployment and monitoring stage