When you want to find machine learning raschka, you may need to consider between many choices. Finding the best machine learning raschka is not an easy task. In this post, we create a very short list about top 7 the best machine learning raschka for you. You can check detail product features, product specifications and also our voting for each product. Let’s start with following top 7 machine learning raschka:
Reviews
1. Python: Deeper Insights into Machine Learning
Description
Leverage benefits of machine learning techniques using Python About This Book * Improve and optimise machine learning systems using effective strategies. * Develop a strategy to deal with a large amount of data. * Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn * Learn to write clean and elegant Python code that will optimize the strength of your algorithms * Uncover hidden patterns and structures in data with clustering * Improve accuracy and consistency of results using powerful feature engineering techniques * Gain practical and theoretical understanding of cutting-edge deep learning algorithms * Solve unique tasks by building models * Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.2. MerryMakers A Ball for Daisy Plush Doll, 8-Inch
Feature
A Ball for Daisy plush toyBased on the 2012 Caldecott-Medal winner, A Ball for Daisy, written and illustrated by Chris Raschka
Safe for all ages, perfect for ages 2-8
Measures 8 inches long
Description
This is the story of a dog, her ball, and her very good friend. A Ball for Daisy, the 2012 Caldecott-Medal winner, is a lovely story told in pictures by Chris Raschka. The Daisy toy from Merry Makers is made of soft plush is a huggable companion, complete with her favorite red ball. Based on the beautiful books beautiful books starring Daisy, written and illustrated by Chris Raschka. Measures 8 inches long. Surface wash only. Safe for all ages--perfect for ages 2-8.3. Little Treasures: Endearments from Around the World
Description
Little Treasures offers a wealth of endearments in fourteen languages to share with your own beloved poppet and petit chou.
4. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
Description
Key Features
- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms
Book Description
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.
If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.
What you will learn
- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis
Table of Contents
- Giving Computers the Ability to Learn from Data
- Training Simple Machine Learning Algorithms for Classification
- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Sets - Data Preprocessing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Embedding a Machine Learning Model into a Web Application
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data - Clustering Analysis
- Implementing a Multilayer Artificial Neural Network from Scratch
- Parallelizing Neural Network Training with TensorFlow
- Going Deeper - The Mechanics of TensorFlow
- Classifying Images with Deep Convolutional Neural Networks
- Modeling Sequential Data using Recurrent Neural Networks
5. Python Machine Learning, 1st Edition
Feature
Python Machine LearningDescription
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
About This Book
- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization
- Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
- Ask and answer tough questions of your data with robust statistical models, built for a range of datasets
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
- Explore how to use different machine learning models to ask different questions of your data
- Learn how to build neural networks using Pylearn 2 and Theano
- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
- Discover how to embed your machine learning model in a web application for increased accessibility
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Organize data using effective pre-processing techniques
- Get to grips with sentiment analysis to delve deeper into textual and social media data
In Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
6. Reading Picture Books with Children: How to Shake Up Storytime and Get Kids Talking about What They See
Feature
Charlesbridge PublishingDescription
A new, interactive approach to storytime, The Whole Book Approach was developed in conjunction with the Eric Carle Museum of Picture Book Art and expert author Megan Dowd Lambert's graduate work in children's literature at Simmons College, offering a practical guide for reshaping storytime and getting kids to think with their eyes.
Traditional storytime often offers a passive experience for kids, but the Whole Book approach asks the youngest of readers to ponder allaspects of a picture book and to use their critical thinking skills. Using classic examples, Megan asks kids to think about why the trim size of Ludwig Bemelman's Madeline is so generous, or why the typeset in David Wiesner's Caldecott winner,The Three Pigs, appears to twist around the page, or why books like ChrisVan Allsburg's The Polar Express and Eric Carle's The Very Hungry Caterpillar are printed landscape instead of portrait. The dynamic discussions that result from this shared reading style range from the profound to the hilarious and will inspire adults to make children's responses to text, art, and design an essential part of storytime.
7. Machine Learning mit Python und Scikit-Learn und TensorFlow