Introduction to data for machine learning
|
The power of machine learning models comes from the data that is used to train them. Through content and exercises, we explore how to understand your data, how to encode it so that the computer can interpret it properly, how to clean any errors, and tips that will help you create high performance models.
Visualize large datasets with Exploratory Data Analysis (EDA)., Clean the errors in a dataset., Predict unknown values using numeric and categorical data.
Prerequisites
Some familiarity with machine learning concepts – for example, models and cost – is helpful, but not essential
User Reviews
Be the first to review “Introduction to data for machine learning”
Related Products
Building Generative Adversarial Networks
Learn to design and implement GANs for generating realistic data samples in various applications.
Natural Language Processing
Explore natural language processing methods to enable machines to understand and generate human language.
AI For Business Leaders
Explore the impact of AI on business strategy and decision-making for leaders in various industries.
Introduction to Python for AI Programmers
Learn Python programming with a focus on applications in AI and machine learning development.
Generative AI Fundamentals
Explore the core concepts of generative AI, including types, models, and use cases in various domains.
AI for Healthcare
Understand the applications of AI in the healthcare sector, including diagnosis, treatment, and patient management.
There are no reviews yet.