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- (Reference Guide) Python Machine Learning - Second Edition
Book Description
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.
If you’ve read the first edition of this book, you’ll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.
What You Will Learn
- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Master machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Ask new questions of your data through machine learning models and neural networks
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis
Table of Contents
1: Giving Computers the Ability to Learn from Data
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- Introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Summary
2: Training Simple Machine Learning Algorithms for Classification
- Artificial neurons – a brief glimpse into the early history of machine learning
- Implementing a perceptron learning algorithm in Python
- Adaptive linear neurons and the convergence of learning
- Summary
3: A Tour of Machine Learning Classifiers Using scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn – training a perceptron
- Modeling class probabilities via logistic regression
- Maximum margin classification with support vector machines
- Solving nonlinear problems using a kernel SVM
- Decision tree learning
- K-nearest neighbors – a lazy learning algorithm
- Summary
4: Building Good Training Sets – Data Preprocessing
- Dealing with missing data
- Handling categorical data
- Partitioning a dataset into separate training and test sets
- Bringing features onto the same scale
- Selecting meaningful features
- Assessing feature importance with random forests
- Summary
5: Compressing Data via Dimensionality Reduction
- Unsupervised dimensionality reduction via principal component analysis
- Supervised data compression via linear discriminant analysis
- Using kernel principal component analysis for nonlinear mappings
- Summary
6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Streamlining workflows with pipelines
- Using k-fold cross-validation to assess model performance
- Debugging algorithms with learning and validation curves
- Fine-tuning machine learning models via grid search
- Looking at different performance evaluation metrics
- Dealing with class imbalance
- Summary
7: Combining Different Models for Ensemble Learning
- Learning with ensembles
- Combining classifiers via majority vote
- Bagging – building an ensemble of classifiers from bootstrap samples
- Leveraging weak learners via adaptive boosting
- Summary
8: Applying Machine Learning to Sentiment Analysis
- Preparing the IMDb movie review data for text processing
- Introducing the bag-of-words model
- Training a logistic regression model for document classification
- Working with bigger data – online algorithms and out-of-core learning
- Topic modeling with Latent Dirichlet Allocation
- Summary
9: Embedding a Machine Learning Model into a Web Application
- Serializing fitted scikit-learn estimators
- Setting up an SQLite database for data storage
- Developing a web application with Flask
- Turning the movie review classifier into a web application
- Deploying the web application to a public server
- Summary
10: Predicting Continuous Target Variables with Regression Analysis
- Introducing linear regression
- Exploring the Housing dataset
- Implementing an ordinary least squares linear regression model
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve – polynomial regression
- Dealing with nonlinear relationships using random forests
- Summary
11: Working with Unlabeled Data – Clustering Analysis
- Grouping objects by similarity using k-means
- Organizing clusters as a hierarchical tree
- Locating regions of high density via DBSCAN
- Summary
12: Implementing a Multilayer Artificial Neural Network from Scratch
- Modeling complex functions with artificial neural networks
- Classifying handwritten digits
- Training an artificial neural network
- About the convergence in neural networks
- A few last words about the neural network implementation
- Summary
13: Parallelizing Neural Network Training with TensorFlow
- TensorFlow and training performance
- Training neural networks efficiently with high-level TensorFlow APIs
- Choosing activation functions for multilayer networks
- Summary
14: Going Deeper – The Mechanics of TensorFlow
- Key features of TensorFlow
- TensorFlow ranks and tensors
- Understanding TensorFlow's computation graphs
- Placeholders in TensorFlow
- Variables in TensorFlow
- Building a regression model
- Executing objects in a TensorFlow graph using their names
- Saving and restoring a model in TensorFlow
- Transforming Tensors as multidimensional data arrays
- Utilizing control flow mechanics in building graphs
- Visualizing the graph with TensorBoard
- Summary
15: Classifying Images with Deep Convolutional Neural Networks
- Building blocks of convolutional neural networks
- Putting everything together to build a CNN
- Implementing a deep convolutional neural network using TensorFlow
- Summary
16: Modeling Sequential Data Using Recurrent Neural Networks
- Introducing sequential data
- RNNs for modeling sequences
- Implementing a multilayer RNN for sequence modeling in TensorFlow
- Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
- Project two – implementing an RNN for character-level language modeling in TensorFlow
- Chapter and book summary
SKU | 031032S |
---|---|
Weight | 3.2840 |
Coming Soon | No |
Days of Training | No |
Audience | Student |
Product Family | Partnerware |
Product Type | Print Courseware |
Electronic | No |
ISBN | 1787125933 |
Language | English |
Page Count | 622 |
Curriculum Library | Python |
Year | No |
Manufacturer's Product Code | No |
Current Revision | 1.0 |
---|---|
Revision Notes | No Revision Information Available |
Original Publication Date | 2018-10-17 00:00:00 |
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(Reference Guide) Python Machine Learning - Second Edition eBook
(031032SE) Student Digital Courseware$31.99