Описание: This book will get you to grips with the Spark Python API. You`ll explore how Python can be used with Spark to build scalable and reliable data-intensive applications.
Chapter Goal: Introduce readers to the PySpark environment, walk them through steps to setup the environment and execute some basic operations
Number of pages: 20
Subtopics:
1. Setting up your environment & data
2. Basic operations
Chapter 2: Basic Statistics and Visualizations
Chapter Goal: Introduce readers to predictive model building framework and help them acclimate with basic data operations
Number of pages: 30
Subtopics:
1. Basic Statistics
2. data manipulations/feature engineering
3. Data visualizations
4. Model building framework
Chapter 3: Variable Selection
Chapter Goal: Illustrate the different variable selection techniques to identify the top variables in a dataset and how they can be implemented using PySpark pipelines
Number of pages: 40
Subtopics:
1. Principal Component Analysis
2. Weight of Evidence & Information Value
3. Chi square selector
4. Singular Value Decomposition
5. Voting based approach
Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques
Chapter Goal: Explain and demonstrate supervised machine learning techniques and help the readers to understand the challenges, nuances of model fitting with multiple evaluation metrics
Number of pages: 40
Subtopics:
1. Supervised:
- Linear regression
- Logistic regression
- Decision Trees
- Random Forests
- Gradient Boosting
- Neural Nets
- Support Vector Machine
- One Vs Rest Classifier
- Naive Bayes
2. Model hyperparameter tuning:
- L1 & L2 regularization
- Elastic net
Chapter 5: Model Validation and selecting the best model
Chapter Goal: Illustrate the different techniques used to validate models, demonstrate which technique should be used for a particular model selection task and finally pick the best model out of the candidate models
Number of pages: 30
Subtopics:
1. Model Validation Statistics:
- ROC
- Accuracy
- Precision
- Recall
- F1 Score
- Misclassification
- KS
- Decile
- Lift & Gain
- R square
- Adj
Автор: Mishra Raju Kumar, Raman Sundar Rajan Название: Pyspark SQL Recipes: With Hiveql, Dataframe and Graphframes ISBN: 148424334X ISBN-13(EAN): 9781484243343 Издательство: Springer Рейтинг: Цена: 5487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.
PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes.
On completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.
What You Will Learn
Understand PySpark SQL and its advanced featuresUse SQL and HiveQL with PySpark SQLWork with structured streamingOptimize PySpark SQL Master graphframes and graph processing
Who This Book Is For
Data scientists, Python programmers, and SQL programmers.
Описание: If you are a developer looking to build machine learning models without spending months and years learning machine learning prerequisites, look no further than AutoML. This practical and concise guide will show you how to build automated models for regression and classification, both with traditional algorithms and neural networks.
Описание: The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You`ll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world.
Inside this book you will find all the basic notions to start with Python and all the programming concepts to build machine learning models. With our proven strategies you will write efficient Python codes in less than a week!
Описание: Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
Описание: Imagine a world where you can make a computer program learn for itself? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
DO YOU WANT TO LEARN THE BASICS OF PYTHON PROGRAMMING QUICKLY?
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it.
Some of the topics that we will discuss include:
The Fundamentals of Machine Learning, Deep learning, And Neural Networks
How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You
How To Master Neural Network Implementation Using Different Libraries
How Random Forest Algorithms Are Able To Help Out With Machine Learning
How To Uncover Hidden Patterns And Structures With Clustering
How Recurrent Neural Networks Work And When To Use
The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning
And Much More
This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like.
If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you.
Описание: Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn
Review machine learning fundamentals such as overfitting, underfitting, and regularization.
Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
Apply in-depth linear algebra with PyTorch
Explore PyTorch fundamentals and its building blocks
Work with tuning and optimizing models
Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
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