Python machine learning by example: easy-to-follow examples that get you up and running with machine learning

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	Python Machine Learning By Example Second Edition
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Preface
	Who this book is for
	What this book covers
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Section 1: Fundamentals of Machine Learning
Getting Started with Machine Learning and Python
	Defining machine learning and why we need it
	A very high-level overview of machine learning technology
		Types of machine learning tasks
		A brief history of the development of machine learning algorithms
	Core of machine learning – generalizing with data
		Overfitting, underfitting, and the bias-variance trade-off
		Avoiding overfitting with cross-validation
		Avoiding overfitting with regularization
		Avoiding overfitting with feature selection and dimensionality reduction
	Preprocessing, exploration, and feature engineering
		Missing values
		Label encoding
		One hot encoding
		Scaling
		Polynomial features
		Power transform
		Binning
	Combining models
		Voting and averaging
		Bagging
		Boosting
		Stacking
	Installing software and setting up
		Setting up Python and environments
		Installing the various packages
			NumPy
			SciPy
			Pandas
			Scikit-learn
			TensorFlow
	Summary
	Exercises
Section 2: Practical Python Machine Learning By Example
Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
	How computers understand language - NLP
	Picking up NLP basics while touring popular NLP libraries
		Corpus
		Tokenization
		PoS tagging
		Named-entity recognition
		Stemming and lemmatization
		Semantics and topic modeling
	Getting the newsgroups data
	Exploring the newsgroups data
	Thinking about features for text data
		Counting the occurrence of each word token
		Text preprocessing
		Dropping stop words
		Stemming and lemmatizing words
	Visualizing the newsgroups data with t-SNE
		What is dimensionality reduction?
		t-SNE for dimensionality reduction
	Summary
	Exercises
Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
	Learning without guidance – unsupervised learning
	Clustering newsgroups data using k-means
		How does k-means clustering work?
		Implementing k-means from scratch
		Implementing k-means with scikit-learn
		Choosing the value of k
		Clustering newsgroups data using k-means
	Discovering underlying topics in newsgroups
	Topic modeling using NMF
	Topic modeling using LDA
	Summary
	Exercises
Detecting Spam Email with Naive Bayes
	Getting started with classification
		Types of classification
		Applications of text classification
	Exploring Naïve Bayes
		Learning Bayes' theorem by examples
		The mechanics of Naïve Bayes
		Implementing Naïve Bayes from scratch
		Implementing Naïve Bayes with scikit-learn
	Classification performance evaluation
	Model tuning and cross-validation
	Summary
	Exercise
Classifying Newsgroup Topics with Support Vector Machines
	Finding separating boundary with support vector machines
		Understanding how SVM works through different use cases
			Case 1 – identifying a separating hyperplane
			Case 2 – determining the optimal hyperplane
			Case 3 – handling outliers
		Implementing SVM
			Case 4 – dealing with more than two classes
		The kernels of SVM
			Case 5 – solving linearly non-separable problems
		Choosing between linear and RBF kernels
	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
		Constructing a decision tree
		The metrics for measuring a split
	Implementing a decision tree from scratch
	Predicting ad click-through with decision tree
	Ensembling decision trees – random forest
		Implementing random forest using TensorFlow
	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
		Getting started with the logistic function
		Jumping from the logistic function to logistic regression
	Training a logistic regression model
		Training a logistic regression model using gradient descent
		Predicting ad click-through with logistic regression using gradient descent
		Training a logistic regression model using stochastic gradient descent
		Training a logistic regression model with regularization
	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
	Learning the essentials of Apache Spark
		Breaking down Spark
		Installing Spark
		Launching and deploying Spark programs
	Programming in PySpark
	Learning on massive click logs with Spark
		Loading click logs
		Splitting and caching the data
		One-hot encoding categorical features
		Training and testing a logistic regression model
	Feature engineering on categorical variables with Spark
		Hashing categorical features
		Combining multiple variables – feature interaction
	Summary
	Exercises
Stock Price Prediction with Regression Algorithms
	Brief overview of the stock market and stock prices
	What is regression?
	Mining stock price data
		Getting started with feature engineering
		Acquiring data and generating features
	Estimating with linear regression
		How does linear regression work?
		Implementing linear regression
	Estimating with decision tree regression
		Transitioning from classification trees to regression trees
		Implementing decision tree regression
		Implementing regression forest
	Estimating with support vector regression
		Implementing SVR
	Estimating with neural networks
		Demystifying neural networks
		Implementing 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 practice 15 – choosing the right algorithm(s) to start with
			Naïve Bayes
			Logistic regression
			SVM
			Random forest (or decision tree)
			Neural networks
		Best practice 16 – reducing overfitting
		Best practice 17 – diagnosing overfitting and underfitting
		Best practice 18 – modeling on large-scale datasets
	Best practices in the deployment and monitoring stage
		Best practice 19 – saving, loading, and reusing models
		Best practice 20 – monitoring model performance
		Best practice 21 – updating models regularly
	Summary
	Exercises
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