Advanced Machine Learning With Python John Hearty Pdf

Machine learning with python book free, deals with the necessary methods and algorithms to provide artificial intelligence. These formulate different learning objectives, address diverse application areas, and make different demands on existing data. Search companies like Google use machine learning for their Adword or Adsense advertisement systems. For example, human beings are not smart enough to rate the parameters in determining the relevance of a search-result page. But this is what machines can do. They can take in complex data and derive patterns essential for optimization of the business processes of the company.

The Advanced Machine Learning With Python Pdf teaches the formalization of learning problems, methods for dimensionality reduction and input engineering as well as ensemble methods. Participants will be familiar with the Python Machine Learning tools following the training.

The advanced deep learning algorithms covers the current of the most generally used machine learning algorithms from a programmer point of view, with a special emphasis on practical aspects. While this book is new to python, the first one was very much oriented towards matlab. In contrast, this edition provides a detailed introduction into programming intelligent systems with examples and figures that can be executed and duplicated by the reader on his own computer, and it shows how simple it is to implement complex learning concepts effectively with little effort.”

Machine learning is the ability to learn without specific programming. Training data is analyzed, and features are extracted through algorithms. Machine learning books pdf github systems are trained to classify situations, predict future values, identify similar cases, group things together or determine relationships or correlations between items. Now it has become possible for computers to learn by themselves with the advancement in machine learning algorithms.

About Advanced Machine Learning With Python Pdf

Learning is a crucial factor in intelligence. The realization of intelligent systems by computers, which are not programmed but trained, is the goal of Artificial Intelligence. Machine learning deals with the necessary methods and algorithms to provide artificial intelligence. These formulate different learning objectives, address diverse application areas, and make different demands on existing data.

Anyone who wants to intelligently use more substantial amounts of data to generate added value from them needs an overview of machine learning. On the other hand, a deeper algorithmic understanding is required to estimate effort and to increase success rates through adjustments.

Machine learning, as part of artificial intelligence, is about using the right features to construct the right models for solving a specific task. Models are nothing more than the output of algorithms applied to the data.

We learn which algorithms exist for which tasks and how we can use them with Scikit-learn in Python. We’ll go through advanced aspects, such as scalability of solutions and the combination of models, as well as discussing deep learning, currently the hottest topic in machine learning.

Machine learning doesn’t work for any particular industry; instead, it works in virtually all of them in some way. We have aimed the further education module at all those who are already analyzing data or would like to do more in the future and would like to acquire more competences.

If you want to understand how Python can help answer critical questions about your data, you’ve come to the right place.
Whether you are a beginner or want to deepen your knowledge in the field of data science, this Advanced Machine Learning With Python John Hearty Pdf is an indispensable source of information and well worth the time it will take to read. Now is your chance to advance your knowledge of machine learning with the use of Python.

  • Resolve complex machine learning problems and explore deep learning
  • Learn to use Python code for implementing a range of machine learning algorithms and techniques
  • A practical tutorial that tackles real-world computing problems through a rigorous and effective approach

Who This Book Is For

This Advanced Machine Learning With Python John Hearty Pdf is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!

Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.

What You Will Learn

  • Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
  • Apply your new found skills to solve real problems, through clearly-explained code for every technique and test
  • Automate large sets of complex data and overcome time-consuming practical challenges
  • Improve the accuracy of models and your existing input data using powerful feature engineering techniques
  • Use multiple learning techniques together to improve the consistency of results
  • Understand the hidden structure of datasets using a range of unsupervised techniques
  • Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach
  • Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together

In Detail

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more – all whilst working with real-world applications that include image, music, text, and financial data.

The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.

This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.

By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.

Style and approach

This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Table of contents for Advanced Machine Learning With Python John Hearty Pdf


Advanced Machine Learning with Python
Table of Contents
Advanced Machine Learning with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What is advanced machine learning?
What should you expect from this book?
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions

  1. Unsupervised Machine Learning
    Principal component analysis
    PCA – a primer
    Employing PCA
    Introducing k-means clustering
    Clustering – a primer
    Kick-starting clustering analysis
    Tuning your clustering configurations
    Self-organizing maps
    SOM – a primer
    Employing SOM
    Further reading
    Summary
  2. Deep Belief Networks
    Neural networks – a primer
    The composition of a neural network
    Network topologies
    Restricted Boltzmann Machine
    Introducing the RBM
    Topology
    Training
    Applications of the RBM
    Further applications of the RBM
    Deep belief networks
    Training a DBN
    Applying the DBN
    Validating the DBN
    Further reading
    Summary
  3. Stacked Denoising Autoencoders
    Autoencoders
    Introducing the autoencoder
    Topology
    Training
    Denoising autoencoders
    Applying a dA
    Stacked Denoising Autoencoders
    Applying the SdA
    Assessing SdA performance
    Further reading
    Summary
  4. Convolutional Neural Networks
    Introducing the CNN
    Understanding the convnet topology
    Understanding convolution layers
    Understanding pooling layers
    Training a convnet
    Putting it all together
    Applying a CNN
    Further Reading
    Summary
  5. Semi-Supervised Learning
    Introduction
    Understanding semi-supervised learning
    Semi-supervised algorithms in action
    Self-training
    Implementing self-training
    Finessing your self-training implementation
    Improving the selection process
    Contrastive Pessimistic Likelihood Estimation
    Further reading
    Summary
  6. Text Feature Engineering
    Introduction
    Text feature engineering
    Cleaning text data
    Text cleaning with BeautifulSoup
    Managing punctuation and tokenizing
    Tagging and categorising words
    Tagging with NLTK
    Sequential tagging
    Backoff tagging
    Creating features from text data
    Stemming
    Bagging and random forests
    Testing our prepared data
    Further reading
    Summary
  7. Feature Engineering Part II
    Introduction
    Creating a feature set
    Engineering features for ML applications
    Using rescaling techniques to improve the learnability of features
    Creating effective derived variables
    Reinterpreting non-numeric features
    Using feature selection techniques
    Performing feature selection
    Correlation
    LASSO
    Recursive Feature Elimination
    Genetic models
    Feature engineering in practice
    Acquiring data via RESTful APIs
    Testing the performance of our model
    Twitter
    Translink Twitter
    Consumer comments
    The Bing Traffic API
    Deriving and selecting variables using feature engineering techniques
    The weather API
    Further reading
    Summary
  8. Ensemble Methods
    Introducing ensembles
    Understanding averaging ensembles
    Using bagging algorithms
    Using random forests
    Applying boosting methods
    Using XGBoost
    Using stacking ensembles
    Applying ensembles in practice
    Using models in dynamic applications
    Understanding model robustness
    Identifying modeling risk factors
    Strategies to managing model robustness
    Further reading
    Summary
  9. Additional Python Machine Learning Tools
    Alternative development tools
    Introduction to Lasagne
    Getting to know Lasagne
    Introduction to TensorFlow
    Getting to know TensorFlow
    Using TensorFlow to iteratively improve our models
    Knowing when to use these libraries
    Further reading
    Summary
    A. Chapter Code Requirements
    Index

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