Python Machine Learning By Example - Third Edition: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Cover
Copyright
Packt Page
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with Machine Learning and Python
	An introduction to machine learning
	Understanding why we need machine learning
	Differentiating between machine learning and automation
	Machine learning applications
	Knowing the prerequisites
	Getting started with three types of machine learning
	A brief history of the development of machine learning algorithms
	Digging into the core of machine learning
		Generalizing with data
		Overfitting, underfitting, and the bias-variance trade-off
			Overfitting
			Underfitting
			The bias-variance trade-off
		Avoiding overfitting with cross-validation
		Avoiding overfitting with regularization
		Avoiding overfitting with feature selection and dimensionality reduction
	Data preprocessing and feature engineering
		Preprocessing and exploration
		Dealing with missing values
		Label encoding
		One-hot encoding
		Scaling
		Feature engineering
		Polynomial transformation
		Power transforms
		Binning
	Combining models
		Voting and averaging
		Bagging
		Boosting
		Stacking
	Installing software and setting up
		Setting up Python and environments
		Installing the main Python packages
			NumPy
			SciPy
			Pandas
			Scikit-learn
			TensorFlow
		Introducing TensorFlow 2
	Summary
	Exercises
Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes
	Getting started with classification
		Binary classification
		Multiclass classification
		Multi-label classification
	Exploring Naïve Bayes
		Learning Bayes' theorem by example
		The mechanics of Naïve Bayes
	Implementing Naïve Bayes
		Implementing Naïve Bayes from scratch
		Implementing Naïve Bayes with scikit-learn
	Building a movie recommender with Naïve Bayes
	Evaluating classification performance
	Tuning models with cross-validation
	Summary
	Exercise
	References
Chapter 3: Recognizing Faces with Support Vector Machine
	Finding the separating boundary with SVM
		Scenario 1 – identifying a separating hyperplane
		Scenario 2 – determining the optimal hyperplane
		Scenario 3 – handling outliers
		Implementing SVM
		Scenario 4 – dealing with more than two classes
		Scenario 5 – solving linearly non-separable problems with kernels
		Choosing between linear and RBF kernels
	Classifying face images with SVM
		Exploring the face image dataset
		Building an SVM-based image classifier
		Boosting image classification performance with PCA
	Fetal state classification on cardiotocography
	Summary
	Exercises
Chapter 4: Predicting Online Ad Click-Through with Tree-Based Algorithms
	A brief overview of ad click-through prediction
	Getting started with two types of data – numerical and categorical
	Exploring a decision tree from the root to the leaves
		Constructing a decision tree
		The metrics for measuring a split
			Gini Impurity
			Information Gain
	Implementing a decision tree from scratch
	Implementing a decision tree with scikit-learn
	Predicting ad click-through with a decision tree
	Ensembling decision trees – random forest
	Ensembling decision trees – gradient boosted trees
	Summary
	Exercises
Chapter 5: Predicting Online Ads 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
		Feature selection using L1 regularization
	Training on large datasets with online learning
	Handling multiclass classification
	Implementing logistic regression using TensorFlow
	Feature selection using random forest
	Summary
	Exercises
Chapter 6: 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
Chapter 7: Predicting Stock Prices with Regression Algorithms
	A 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 from scratch
		Implementing linear regression with scikit-learn
		Implementing linear regression with TensorFlow
	Estimating with decision tree regression
		Transitioning from classification trees to regression trees
		Implementing decision tree regression
		Implementing a regression forest
	Estimating with support vector regression
		Implementing SVR
	Evaluating regression performance
	Predicting stock prices with the three regression algorithms
	Summary
	Exercises
Chapter 8: Predicting Stock Prices with Artificial Neural Networks
	Demystifying neural networks
		Starting with a single-layer neural network
			Layers in neural networks
		Activation functions
		Backpropagation
		Adding more layers to a neural network: DL
	Building neural networks
		Implementing neural networks from scratch
		Implementing neural networks with scikit-learn
		Implementing neural networks with TensorFlow
	Picking the right activation functions
	Preventing overfitting in neural networks
		Dropout
		Early stopping
	Predicting stock prices with neural networks
		Training a simple neural network
		Fine-tuning the neural network
	Summary
	Exercise
Chapter 9: Mining the 20 Newsgroups Dataset with Text Analysis Techniques
	How computers understand language – NLP
		What is NLP?
		The history of NLP
		NLP applications
	Touring popular NLP libraries and picking up NLP basics
		Installing famous NLP libraries
		Corpora
		Tokenization
		PoS tagging
		NER
		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
		Reducing inflectional and derivational forms of words
	Visualizing the newsgroups data with t-SNE
		What is dimensionality reduction?
		t-SNE for dimensionality reduction
	Summary
	Exercises
Chapter 10: Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
	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
Chapter 11: 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 whether to encode categorical features
		Best practice 8 – Deciding whether to select features, and if so, how to do so
		Best practice 9 – Deciding whether to reduce dimensionality, and if so, how to do so
		Best practice 10 – Deciding whether to rescale features
		Best practice 11 – Performing feature engineering with domain expertise
		Best practice 12 – Performing feature engineering without domain expertise
		Binarization
		Discretization
		Interaction
		Polynomial transformation
		Best practice 13 – Documenting how each feature is generated
		Best practice 14 – Extracting features from text data
		Tf and tf-idf
		Word embedding
		Word embedding with pre-trained models
	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
		Saving and restoring models using pickle
		Saving and restoring models in TensorFlow
		Best practice 20 – Monitoring model performance
		Best practice 21 – Updating models regularly
	Summary
	Exercises
Chapter 12: Categorizing Images of Clothing with Convolutional Neural Networks
	Getting started with CNN building blocks
		The convolutional layer
		The nonlinear layer
		The pooling layer
	Architecting a CNN for classification
	Exploring the clothing image dataset
	Classifying clothing images with CNNs
		Architecting the CNN model
		Fitting the CNN model
		Visualizing the convolutional filters
	Boosting the CNN classifier with data augmentation
		Horizontal flipping for data augmentation
		Rotation for data augmentation
		Shifting for data augmentation
	Improving the clothing image classifier with data augmentation
	Summary
	Exercises
Chapter 13: Making Predictions with Sequences Using Recurrent Neural Networks
	Introducing sequential learning
	Learning the RNN architecture by example
		Recurrent mechanism
		Many-to-one RNNs
		One-to-many RNNs
		Many-to-many (synced) RNNs
		Many-to-many (unsynced) RNNs
	Training an RNN model
	Overcoming long-term dependencies with Long Short-Term Memory
	Analyzing movie review sentiment with RNNs
		Analyzing and preprocessing the data
		Building a simple LSTM network
		Stacking multiple LSTM layers
	Writing your own War and Peace with RNNs
		Acquiring and analyzing the training data
		Constructing the training set for the RNN text generator
		Building an RNN text generator
		Training the RNN text generator
	Advancing language understanding with the Transformer model
		Exploring the Transformer's architecture
		Understanding self-attention
	Summary
	Exercises
Chapter 14: Making Decisions in Complex Environments with Reinforcement Learning
	Setting up the working environment
		Installing PyTorch
		Installing OpenAI Gym
	Introducing reinforcement learning with examples
		Elements of reinforcement learning
		Cumulative rewards
		Approaches to reinforcement learning
	Solving the FrozenLake environment with dynamic programming
		Simulating the FrozenLake environment
		Solving FrozenLake with the value iteration algorithm
		Solving FrozenLake with the policy iteration algorithm
	Performing Monte Carlo learning
		Simulating the Blackjack environment
		Performing Monte Carlo policy evaluation
		Performing on-policy Monte Carlo control
	Solving the Taxi problem with the Q-learning algorithm
		Simulating the Taxi environment
		Developing the Q-learning algorithm
	Summary
	Exercises
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Index