The Data Science Workshop - Helion
Tytuł oryginału: The Data Science Workshop
ISBN: 9781838983086
stron: 817, Format: ebook
Data wydania: 2020-01-29
Księgarnia: Helion
Cena książki: 109,00 zł
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.
Osoby które kupowały "The Data Science Workshop", wybierały także:
- Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 199,00 zł, (69,65 zł -65%)
- NLP. Kurs video. Analiza danych tekstowych w j 144,86 zł, (52,15 zł -64%)
- Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zł, (67,05 zł -55%)
- Excel 2013. Kurs video. Poziom drugi. Przetwarzanie i analiza danych 79,00 zł, (35,55 zł -55%)
- Google Analytics od podstaw. Analiza wp 69,00 zł, (34,50 zł -50%)
Spis treści
The Data Science Workshop. A New, Interactive Approach to Learning Data Science eBook -- spis treści
- 1. Introduction to Data Science in Python
- 2. Regression
- 3. Binary Classification
- 4. Multiclass Classification with RandomForest
- 5. Performing Your First Cluster Analysis
- 6. How to Assess Performance
- 7. The Generalization of Machine Learning Models
- 8. Hyperparameter Tuning
- 9. Interpreting a Machine Learning Model
- 10. Analyzing a Dataset
- 11. Data Preparation
- 12. Feature Engineering
- 13. Imbalanced Datasets
- 14. Dimensionality Reduction
- 15. Ensemble Learning
- 16. Machine Learning Pipelines
- 17. Automated Feature Engineering