English | 2020 | ISBN: 978-180056692 | 824 Pages | PDF | 25 MB
Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms
Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities.
The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search.
Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.
By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
What you will learn
- Explore the key differences between supervised learning and unsupervised learning
- Manipulate and analyze data using scikit-learn and pandas libraries
- Understand key concepts such as regression, classification, and clustering
- Discover advanced techniques to improve the accuracy of your model
- Understand how to speed up the process of adding new features
- Simplify your machine learning workflow for production