Name: The Data Science Workshop: A New, Interactive Approach to Learning Data Science (en Inglés)
Price: 58160.00 CLP
Availability: InStock
Author: Anthony So; Thomas V. Joseph; Robert Thas John; Andrew Worsley; Dr. Samuel Asare
ISBN: 9781838981266

portada The Data Science Workshop: A New, Interactive Approach to Learning Data Science (en Inglés)

Formato

Libro Físico

Editorial

Packt Publishing

Autor

Anthony So; Thomas V. Joseph; Robert Thas John; Andrew Worsley; Dr. Samuel Asare

Año

2020

Idioma

Inglés

N° páginas

818

Encuadernación

Tapa Blanda

ISBN13

9781838981266

Categorías

Lenguajes de programación scripting

The Data Science Workshop: A New, Interactive Approach to Learning Data Science (en Inglés)

Anthony So; Thomas V. Joseph; Robert Thas John; Andrew Worsley; Dr. Samuel Asare (Autor) · Packt Publishing · Tapa Blanda

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Reseña del libro "The Data Science Workshop: A New, Interactive Approach to Learning Data Science (en Inglés)"

Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book Description You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is for Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.Table of Contents Introduction to Data Science in Python Regression Binary Classification Multiclass Classification with RandomForest Performing Your First Cluster Analysis How to Assess Performance The Generalization of Machine Learning Models Hyperparameter Tuning Interpreting a Machine Learning Model Analyzing a Dataset Data Preparation Feature Engineering Imbalanced Datasets Dimensionality Reduction Ensemble Learning Machine Learning Pipelines Automated Feature Engineering