Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. 

You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well.   

There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Key Features

• Build deep learning models with transfer learning principles in Python
• implement transfer learning to solve real-world research problems
• Perform complex operations such as image captioning neural style transfer

Book Description

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).

By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.

What you will learn

• Set up your own DL environment with graphics processing unit (GPU) and Cloud support
• Delve into transfer learning principles with ML and DL models
• Explore various DL architectures, including CNN, LSTM, and capsule networks
• Learn about data and network representation and loss functions
• Get to grips with models and strategies in transfer learning
• Walk through potential challenges in building complex transfer learning models from scratch
• Explore real-world research problems related to computer vision and audio analysis
• Understand how transfer learning can be leveraged in NLP

Who this book is for

Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

Table of Contents

1. Machine Learning Fundamentals
2. Deep Learning Essentials
3. Understanding Deep Learning Architectures
4. Transfer Learning Fundamentals
5. Unleash the Power of Transfer Learning
6. Image Recognition and Classification
7. Text Document Categorization
8. Audio Identification and Categorization
9. Deep Dream
10. Neural Style Transfer
11. Automated Image Caption Generator
12. Image Colorization

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

• Execute end-to-end machine learning projects and systems
  
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
  
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.
  

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R

About This Book

• A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data
• Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.
• Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering.

Who This Book Is For

It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.

What You Will Learn

• Learn how to tap into data from diverse social media platforms using the R ecosystem
• Use social media data to formulate and solve real-world problems
• Analyze user social networks and communities using concepts from graph theory and network analysis
• Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels
• Understand the art of representing actionable insights with effective visualizations
• Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on
• Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more

In Detail

The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.

The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.

Style and approach

This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so

Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.

Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

What You Will Learn:

• Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure
  
• Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews
  
• Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern
  

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.

About This Book

• Build your confidence with R and find out how to solve a huge range of data-related problems
• Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
• Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis

Who This Book Is For

Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science

What You Will Learn

• Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
• Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
• Solve interesting real-world problems using machine learning and R as the journey unfolds
• Write reusable code and build complete machine learning systems from the ground up
• Learn specialized machine learning techniques for text mining, social network data, big data, and more
• Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
• Evaluate and improve the performance of machine learning models
• Learn specialized machine learning techniques for text mining, social network data, big data, and more

In Detail

R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it...

This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.

In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.

For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.

This Learning Path has been curated from three Packt products:

• R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
• Machine Learning with R Learning - Second Edition By Brett Lantz
• Mastering Machine Learning with R By Cory Lesmeister

Style and approach

This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

About This Book

• Get to grips with the concepts of machine learning through exciting real-world examples
• Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning
• Learn to build your own machine learning system with this example-based practical guide

Who This Book Is For

If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.

What You Will Learn

• Utilize the power of R to handle data extraction, manipulation, and exploration techniques
• Use R to visualize data spread across multiple dimensions and extract useful features
• Explore the underlying mathematical and logical concepts that drive machine learning algorithms
• Dive deep into the world of analytics to predict situations correctly
• Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
• Write reusable code and build complete machine learning systems from the ground up
• Solve interesting real-world problems using machine learning and R as the journey unfolds
• Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science

In Detail

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.

This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.

You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.

Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.

Style and approach

The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application.

Use over 100 solutions to analyze data and build predictive models

About This Book

• Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm
• Compare the differences between different regression methods to discover how they solve problems
• Incorporate R and Hadoop to solve machine learning problems on big data
• Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science

Who This Book Is For

Are you interested in understanding machine learning concepts and building real-time machine learning projects with R, but don’t know where to start? Then, this is the perfect course for you!

What You Will Learn

• Create and inspect the transaction dataset and perform association analysis with the Apriori algorithm
• Predict possible churn users with the classification approach
• Implement the clustering method to segment customer data
• Compress images with the dimension reduction method
• Build a product recommendation system

In Detail

This course will take you from the very basics of R to creating insightful machine learning models with R.

We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.

This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.

We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.

We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.

We will discover associated terms and underline frequent patterns from transaction data.

We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.

By the end of this course, we will build our own project in the e-commerce domain. Then, we will tackle the problem of personalization.

Style and approach

This course is full of hands-on recipes for machine learning with R. Each topic is fully explained, followed by step-by-step and practical examples.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

• R Machine Learning Solutions by Yu-Wei, Chiu (David Chiu)
• Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
• R Machine Learning By Example by Raghav Bali and Dipanjan Sarkar