Python Machine Learning Cookbook - Second Edition, Ciaburro Giuseppe, Joshi Prateek

• Learn to create a digital prototype of a real model using hands-on examples
• Evaluate the performance and output of your prototype using simulation modeling techniques
• Understand various statistical and physical simulations to improve systems using Python

Book Description

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.

Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.

By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

What you will learn

• Gain an overview of the different types of simulation models
• Get to grips with the concepts of randomness and data generation process
• Understand how to work with discrete and continuous distributions
• Work with Monte Carlo simulations to calculate a definite integral
• Find out how to simulate random walks using Markov chains
• Obtain robust estimates of confidence intervals and standard errors of population parameters
• Discover how to use optimization methods in real-life applications
• Run efficient simulations to analyze real-world systems

Who this book is for

Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.

• Updated for JIRA 7, this book covers all the new features introduced in JIRA 7 with a dedicated chapter on JIRA Service Desk--one of the biggest new add-ons to JIRA
• This book lays a strong foundation to work with agile projects in JIRA from both the administrator and end user's perspective
• Learn to solve challenging data science problems by building powerful machine learning models using Python

Book Description

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:

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

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

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.

Key Features

• 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 understand the image content and recognize objects in an image

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 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.

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 objects in an image and understand the content
• Reconstruct a 3D map from images
• Build an augmented reality application

• 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

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.

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

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.