Book Image

Python: Deeper Insights into Machine Learning

By : David Julian, Sebastian Raschka, John Hearty
Book Image

Python: Deeper Insights into Machine Learning

By: David Julian, Sebastian Raschka, John Hearty

Overview of this book

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. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

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Python: Deeper Insights into Machine Learning
David Julian | Sebastian Raschka | John Hearty
Aug, 2016 | 15 hours 1 minutes
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Table of Contents (6 chapters)
Preface
Preface
What this learning path covers
What you need for this learning path
Who this learning path is for
Reader feedback
Customer support
Free Chapter
1
1. Module 1
1. Module 1
1. Giving Computers the Ability to Learn from Data
2. Training Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-learn
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
11. Working with Unlabeled Data – Clustering Analysis
12. Training Artificial Neural Networks for Image Recognition
13. Parallelizing Neural Network Training with Theano
2
2. Module 2
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
3. Module 3
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
4
A. Biblography
A. Biblography
5
Index
Index

Chapter 5. Semi-Supervised Learning

Introduction

In previous chapters, we've tackled a range of data challenges using advanced techniques. In each case, we've applied our techniques to datasets with reasonable success.

In many regards, though, we've had it pretty easy. Our data has been largely derived from canonical and well-prepared sources so we haven't had to do a great deal of preparation. In the real world, though, there are few datasets like this (except, perhaps, the ones that we're able to specify ourselves!). In particular, it is rare and improbable to come across a dataset in the wild, which has class labels available. Without labels on a sufficient portion of the dataset, we find ourselves unable to build a classifier that can accurately predict labels on validation or test data. So, what do we do?

The common solution is attempt to tag our data manually; not only is this time-consuming, but it also suffers from certain types of human error (which...

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