Preface
by John Hearty, David Julian, Sebastian Raschka
Python: Deeper Insights into Machine Learning
- Python: Deeper Insights into Machine Learning
- Table of Contents
- Python: Deeper Insights into Machine Learning
- Python: Deeper Insights into Machine Learning
- Credits
- Preface
- 1. Module 1
- 1. Giving Computers the Ability to Learn from Data
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Summary
- 2. Training Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using Scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn
- 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
- 5. Compressing Data via Dimensionality Reduction
- 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- 7. Combining Different Models for Ensemble Learning
- 8. Applying Machine Learning to Sentiment Analysis
- 9. Embedding a Machine Learning Model into a Web Application
- 10. Predicting Continuous Target Variables with Regression Analysis
- Introducing a simple linear regression model
- 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
- Summary
- 11. Working with Unlabeled Data – Clustering Analysis
- 12. Training Artificial Neural Networks for Image Recognition
- Modeling complex functions with artificial neural networks
- Classifying handwritten digits
- Training an artificial neural network
- Developing your intuition for backpropagation
- Debugging neural networks with gradient checking
- Convergence in neural networks
- Other neural network architectures
- A few last words about neural network implementation
- Summary
- 13. Parallelizing Neural Network Training with Theano
- 1. Giving Computers the Ability to Learn from Data
- 2. Module 2
- 1. Thinking in Machine Learning
- 2. Tools and Techniques
- 3. Turning Data into Information
- 4. Models – Learning from Information
- 5. Linear Models
- 6. Neural Networks
- 7. Features – How Algorithms See the World
- 8. Learning with Ensembles
- 9. Design Strategies and Case Studies
- 3. Module 3
- 1. Unsupervised Machine Learning
- 2. Deep Belief Networks
- 3. Stacked Denoising Autoencoders
- 4. Convolutional Neural Networks
- 5. Semi-Supervised Learning
- 6. Text Feature Engineering
- 7. Feature Engineering Part II
- 8. Ensemble Methods
- 9. Additional Python Machine Learning Tools
- 10. Chapter Code Requirements
- A. Biblography
- Index
Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace .It is one of the fastest growing trends in modern computing and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a Learning Path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems
Module 1, Python Machine Learning, discusses the essential machine algorithms for classification and provides practical examples using scikit-learn. It teaches you to prepare variables of different types and also speaks about polynomial regression and tree-based approaches. This module focuses on open source Python library that allows us to utilize multiple cores of modern GPUs.
Module 2, Designing Machine Learning Systems with Python, acquaints you with large library of packages for machine learning tasks. It introduces broad topics such as big data, data properties, data sources, and data processing .You will further explore models that form the foundation of many advanced nonlinear techniques. This module will help you in understanding model selection and parameter tuning techniques that could help in various case studies.
Module 3, Advanced Machine Learning with Python, helps you to build your skill with deep architectures by using stacked denoising autoencoders. This module is a blend of semi-supervised learning techniques, RBM and DBN algorithms .Further this focuses on tools and techniques which will help in making consistent working process.
-
No Comment