Understanding underlying trends and outliers in data is a necessary step to do proper data preparation and feature engineering for subsequent machine learning tasks.
In this course, Exploratory Data Analysis with AWS Machine Learning, you’ll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps.
What you will learn in this beginner level AWS machine learning tutorial:
First, you’ll explore how to understand data trends and distribution using basic statistics.
Next, you’ll discover how to visualize your dataset to understand the overall patterns.
Finally, you’ll learn how to prepare your data for the machine learning pipeline by doing preprocessing and feature engineering.
When you’re finished with this course, you’ll have the skills and knowledge of exploratory data analysis needed to achieve AWS Machine Learning specialty certification.
Mohammed Osman is a senior software engineer who started coding at the age of 13. Mohammed worked in various industries, including telecommunication, accounting, banking, health, and assurance. Mohammed's core skillset is a .NET ecosystem with a strong focus on C#, Azure, and Data Science. Mohammed also enjoys the soft-side of software engineering and leads scrum teams. Mohammed runs a blog with the message "Making your code smart and your career smarter." He shares tips and techniques to improve your code and valuable career pieces of advice in his blog.
Course Overview Hi, everyone. My name is Mohammed Osman, and welcome to my course, Exploratory Data Analysis with AWS Machine Learning. I am a software developer and machine learning enthusiast at Smarter Code. Machine learning is booming everywhere; however, machine learning is not much useful without careful data analysis where we understand the underlying data trends and patterns and data preparation where we fix the issues we found in our data using data preparation techniques. In this course, we are going to follow a hands‑on approach to learn how to do exploratory data analysis using AWS, a very important domain of the AWS Machine Learning ‑ Specialty exam. Some of the major topics that we will cover include what are the available exploratory data analysis techniques in AWS, how to analyze your data using descriptive statistics and reason behind it, how to use different types of visualization techniques to understand your data distribution, what are different challenges with the data, and how to fix these challenges using a hands‑on approach. By the end of this course, you will know how to analyze, visualize, and prepare your data for machine learning tests, and you will gain the required skills in the exploratory data analysis domain in AWS Certified Machine Learning ‑ Specialty. Before beginning the course, you should be familiar with basics of Python, basics of AWS, on some basics of machine learning. From here, you should feel comfortable diving into machine learning modeling with courses on machine learning model training and evaluation. I hope you will join me on this journey to learn exploratory data analysis with the Exploratory Data analysis with AWS Machine Learning, here at Pluralsight.