دانلود کتاب Python Machine Learning By Example

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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
Other Books You May Enjoy
Index