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 loves software development and loves to give back to the community, Mohammed takes Albert Einstien statement: "Try not to become a man of success, but rather a man of value." as a core life guide.
Course Overview [Autogenerated] Hi, everyone. My name is Mohamed Osman and welcome to my course exploratory data analysis with AWS Machine Learning I am a software developer on the machine learning a toothy assist at smarter called Machine Learning is pulling everywhere. However, machine learning is not much useful without careful data analysis where we understand the underlying that transcend veterans on 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 except territory. Debt Analyst is using AWS are very important domain off the AWS machine learning specialty Example. Some of the major topics that you will cover include what are the available except territory that analysts techniques in AWS have to analyze your data using descriptive statistics and reason behind it. How to use different type of a visualization techniques to understand your data distribution. What are different challenges with the data on 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 get the required skills in the except territory that analysts demand in AWS certified machine learning specialities 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 Edible es machine Learning here, a rural site.