Book description
Leverage benefits of machine learning techniques using Python.
About This Book
 Improve and optimise machine learning systems using effective strategies.
 Develop a strategy to deal with a large amount of data.
 Use of Python code for implementing a range of machine learning algorithms and techniques.
Who This Book Is For
This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its realworld application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.
What You Will Learn
 Learn to write clean and elegant Python code that will optimize the strength of your algorithms
 Uncover hidden patterns and structures in data with clustering
 Improve accuracy and consistency of results using powerful feature engineering techniques
 Gain practical and theoretical understanding of cuttingedge deep learning algorithms
 Solve unique tasks by building models
 Get grips on the machine learning design process
In Detail
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 realworld complexities of modern machine learning with innovative and cuttingedge 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 scikitlearn 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.
Style and approach
This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a stepbystep manner. Each topic is well explained with realworld applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques
Publisher resources
Table of contents

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 Scikitlearn
 Choosing a classification algorithm
 First steps with scikitlearn
 Modeling class probabilities via logistic regression
 Maximum margin classification with support vector machines
 Solving nonlinear problems using a kernel SVM
 Decision tree learning
 Knearest 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. SemiSupervised 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
Product information
 Title: Python: Deeper Insights into Machine Learning
 Author(s):
 Release date: August 2016
 Publisher(s): Packt Publishing
 ISBN: 9781787128576
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