Learn to solve challenging data science problems by building powerful machine learning models using Python

About This Book

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• This practical tutorial tackles real-world computing problems through a rigorous and effective approach
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and 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. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

What You Will Learn

• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with modern state-of-the-art large-scale machine learning techniques
• Learn to use Python code to implement a range of machine learning algorithms and techniques

In Detail

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• Python Machine Learning Cookbook by Prateek Joshi
• Advanced Machine Learning with Python by John Hearty
• Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

Style and approach

This course is a smooth learning path that will teach you how to get st

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.

Key Features

• Completely updated and revised to Python 3.x
• New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering
• Learn more about deep learning algorithms, machine learning data pipelines, and chatbots

Book Description

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.

This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.

Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.

What you will learn

• Understand what artificial intelligence, machine learning, and data science are
• Explore the most common artificial intelligence use cases
• Learn how to build a machine learning pipeline
• Assimilate the basics of feature selection and feature engineering
• Identify the differences between supervised and unsupervised learning
• Discover the most recent advances and tools offered for AI development in the cloud
• Develop automatic speech recognition systems and chatbots
• Apply AI algorithms to time series data

Who this book is for

The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Table of Contents

1. Introduction to Artificial Intelligence
2. Fundamental Use Cases for Artificial Intelligence
3. Machine Learning Pipelines
4. Feature Selection and Feature Engineering
5. Classification and Regression Using Supervised Learning
6. Predictive Analytics with Ensemble Learning
7. Detecting Patterns with Unsupervised Learning
8. Building Recommender Systems
9. Logic Programming
10. Heuristic Search Techniques
11. Genetic Algorithms and Genetic Programming
12. Artificial Intelligence on the Cloud
13. Building Games with Artificial Intelligence
14. Building a Speech Recognizer
15. Natural Language Processing
16. Chatbots
17. Sequential Data and Time Series Analysis
18. Image Recognition
19. Neural Networks
20. Deep Learning with Convolutional Neural Networks
21. Recurrent Neural Networks and Other Deep Le

Publisher’s Note: This edition from 2017 is outdated and not compatible with TensorFlow 2.x or any of the most recent updates to Python libraries. A new edition completely updated and revised for 2020 with seven additional chapters that cover RNNs, AI and big data, fundamental use cases, chatbots, and more, is now available.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you

Key Features

• Step into the amazing world of intelligent apps using this comprehensive guide
• Enter the world of Artificial Intelligence, explore it, and create your own applications
• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time

Who This Book Is For

This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.

What you will learn

• Realize different classification and regression techniques
• Understand the concept of clustering and how to use it to automatically segment data
• See how to build an intelligent recommender system
• Understand logic programming and how to use it
• Build automatic speech recognition systems
• Understand the basics of heuristic search and genetic programming
• Develop games using Artificial Intelligence

Book Description

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more.

Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms

Key Features

• Discover best practices for engineering and maintaining OpenCV projects
• Explore important deep learning tools for image classification
• Understand basic image matrix formats and filters

Book Description

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.

This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:

• Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá
• Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi

What you will learn

• Stay up-to-date with algorithmic design approaches for complex computer vision tasks
• Work with OpenCV's most up-to-date API through various projects
• Understand 3D scene reconstruction and Structure from Motion (SfM)
• Study camera calibration and overlay augmented reality (AR) using the ArUco module
• Create CMake scripts to compile your C++ application
• Explore segmentation and feature extraction techniques
• Remove backgrounds from static scenes to identify moving objects for surveillance
• Work with new OpenCV functions to detect and recognize text with Tesseract

Who this book is for

If you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path.

Table of Contents

1. Getting Started with OpenCV
2. An Introduction to the Basics of OpenCV
3. Learning Graphical User Interfaces
4. Delving into Histogram and Filters
5. Automated Optical Inspection, Object Segmentation, and Detection
6. Learning Object Classification
7. Detecting Face Parts and Overlaying Masks
8. Video Surveillance, Background Modeling, and Morphological Operations
9. Learning Object Tracking
10. Developing Segmentation Algorithms for Text Recognition
11. Text Recognition with Tesseract
12. Deep Learning with OpenCV
13. Cartoonifier and Skin Color Analysis on the RaspberryPi
14. Explore Structure from Motion with the SfM

Explore OpenCV 4 to create visually appealing cross-platform computer vision applications

Key Features

• Understand basic OpenCV 4 concepts and algorithms
• Grasp advanced OpenCV techniques such as 3D reconstruction, machine learning, and artificial neural networks
• Work with Tesseract OCR, an open-source library to recognize text in images

Book Description

OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects.

You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module.

By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.

What you will learn

• Install OpenCV 4 on your operating system
• Create CMake scripts to compile your C++ application
• Understand basic image matrix formats and filters
• Explore segmentation and feature extraction techniques
• Remove backgrounds from static scenes to identify moving objects for surveillance
• Employ various techniques to track objects in a live video
• Work with new OpenCV functions for text detection and recognition with Tesseract
• Get acquainted with important deep learning tools for image classification

Who this book is for

If you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, Learn OpenCV 4 by Building Projects for you. Prior knowledge of C++ will help you understand the concepts covered in this book.

Table of Contents

1. Getting started with OpenCV
2. An introduction to the basics of OpenCV
3. Learning graphical user interfaces
4. Delving into histrograms and filters
5. Automated optical inspection, object segmentation and detection
6. Learning object classification
7. Detecting face parts and overlaying masks
8. Video surveillance, background modeling, and morphological operations
9. Learning object tracking
10. Developing segmentation algorithms for text recognition
11. Text recognition with Tesseract
12. Deep Learning with OpenCV

Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV.

Key Features

• Learn how to apply complex visual effects to images with OpenCV 3.x and Python
• Extract features from an image and use them to develop advanced applications
• Build algorithms to help you understand image content and perform visual searches
• Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented reality

Book Description

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease.

We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples.

This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.

What you will learn

• Detect shapes and edges from images and videos
• How to apply filters on images and videos
• Use different techniques to manipulate and improve images
• Extract and manipulate particular parts of images and videos
• Track objects or colors from videos
• Recognize specific object or faces from images and videos
• How to create Augmented Reality applications
• Apply artificial neural networks and machine learning to improve object recognition

Who this book is for

This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

Table of Contents

1. Applying Geometric Transformations to Images
2. Detecting Edges and Applying Image Filters
3. Cartoonizing an Image
4. Detecting and Tracking Different Body Parts
5. Extracting Features from an Image
6. Seam Carving
7. Detecting Shapes and Segmenting an Image
8. Object Tracking
9. Object Recognition
10. Augmented Reality
11. Machine Learning by an Artificial Neural Network

Key Features

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• Learn about perceptrons and see how they are used to build neural networks
• Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques

Book Description

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

What you will learn

• Explore classification algorithms and apply them to the income bracket estimation problem
• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Explore data visualization techniques to interact with your data in diverse ways
• Find out how to build a recommendation engine
• Understand how to interact with text data and build models to analyze it
• Work with speech data and recognize spoken words using Hidden Markov Models
• Analyze stock market data using Conditional Random Fields
• Work with image data and build systems for image recognition and biometric face recognition
• Grasp how to use deep neural networks to build an optical character recognition system

About the Author

Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences.

People from all over the world visit his blog, and he has received more than a million page views from over 200 countries. He has been featured as a guest author in prominent tech magazines. He enjoys blogging about topics, such as Artificial Intelligence, Python programming, abstract mathematics, and cryptography. You can visit his blog at www.prateekvjoshi.com.

He has won many hackathons utilizing a wide variety of technologies. He is an avid coder who is passionate about building game-changing products. He graduated from University of Southern California, and he has worked at companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. You can learn more about him on his personal website at www.prateekj.com.

Table of Contents

1. The Realm of Supervised Learning
2. Constructing a Classifier
3. Predictive Modeling
4. Clustering with Unsupervis

Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

About This Book

• Get to grips with the basics of Computer Vision and image processing
• This is a step-by-step guide to developing several real-world Computer Vision projects using OpenCV 3
• This book takes a special focus on working with Tesseract OCR, a free, open-source library to recognize text in images

Who This Book Is For

If you are a software developer with a basic understanding of Computer Vision and image processing and want to develop interesting Computer Vision applications with Open CV, this is the book for you. Knowledge of C++ is required.

What You Will Learn

• Install OpenCV 3 on your operating system
• Create the required CMake scripts to compile the C++ application and manage its dependencies
• Get to grips with the Computer Vision workflows and understand the basic image matrix format and filters
• Understand the segmentation and feature extraction techniques
• Remove backgrounds from a static scene to identify moving objects for video surveillance
• Track different objects in a live video using various techniques
• Use the new OpenCV functions for text detection and recognition with Tesseract

In Detail

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation.

Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.

Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch.

By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.

Style and approach

This book is a practical guide with lots of tips, and is closely focused on developing Computer vision applications with OpenCV. Beginning with the fundamentals, the complexity increases with each chapter. Sample applications are developed throughout the book that you can execute and use in your own projects.

Build real-world computer vision applications and develop cool demos using OpenCV for Python

About This Book

• Learn how to apply complex visual effects to images using geometric transformations and image filters
• Extract features from an image and use them to develop advanced applications
• Build algorithms to help you understand the image content and perform visual searches

Who This Book Is For

This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

What You Will Learn

• Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
• Detect and track various body parts such as the face, nose, eyes, ears, and mouth
• Stitch multiple images of a scene together to create a panoramic image
• Make an object disappear from an image
• Identify different shapes, segment an image, and track an object in a live video
• Recognize an object in an image and build a visual search engine
• Reconstruct a 3D map from images
• Build an augmented reality application

In Detail

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel.

This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications.

This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples.

The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.

Style and approach

This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.

Apply your existing Python skills to the highly lucrative field of data science and machine learning. Become an expert!

Who This Book Is For

This course is for Python programmers, developers, and data scientists looking to use machine learning algorithms and techniques to create real-world applications. Some familiarity with Python programming will certainly be helpful to play around with the code

What You Will Learn

• Explore a wide variety of machine learning algorithms to solve real-world problems
• Implement classification and predictive modeling in a real-world setting
• Apply your machine learning skills to build interesting apps
• Understand deep learning with TensorFlow

In Detail

ML is becoming increasingly pervasive in the modern data-driven world. This course takes a hands-on approach and demonstrates how you can perform various machine learning tasks on real-world data.

The course starts by talking about various realms in machine learning followed by practical examples. It then moves on to discuss the more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Sentiment Analysis, and Conditional Random Fields. You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results. After you are comfortable with machine learning, this course teaches you how to build real-world machine learning applications step by step. Further, we’ll explore deep learning with TensorFlow, which is currently the hottest topic in data science.

By the end of this course, you should be able to solve real-world data analysis challenges using innovative and cutting-edge machine learning techniques.

Style and approach

With easy-to-follow practical examples, this course will help you gain a grip on each and every aspect of machine learning. Covering all the powerful algorithms of machine learning, we’ll teach you how to build different interesting machine learning applications and finally cover deep learning with TensorFlow.

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:

• Python Machine Learning Cookbook by Prateek Joshi
• Python Machine Learning Solutions by Prateek Joshi
• Python Machine Learning Blueprints by Alexander T. Combs
• Python Machine Learning Projects by Alexander T. Combs
• Deep Learning with TensorFlow by Dan Van Boxel
• Getting Started with TensorFlow by Giancarlo Zaccone
• Python Machine Learning by Sebastian Raschka
• Building Machine Learning Systems with Python - Second Edition by Luis Pedro Coelho and Willi Richert