Certification     Amazon     AWS Certified Machine Learning – Specialty     AWS Certified Machine Learning – Specialty

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Exploring and analyzing data to comprehend its underlying characteristics and patterns becomes increasingly vital as vaster amounts are collected. This is key in formulating the most suitable problems, the solving of which helps achieve real-world business goals.
Use this course to get your head around the programming fundamentals for machine learning in AWS, which form the basis for most data exploratory steps on the AWS platform.
Explore various Python packages used in machine learning and data analysis and become familiar with Jupyter Notebook's fundamental concepts. Then, work with Python and Jupyter Notebook to create a machine learning model.
When you're done, you'll be able to use Jupyter Notebook and various Python packages in machine learning and data analysis. You'll be one step closer to being prepared for the AWS Certified Machine Learning - Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Jupyter Notebook & Python discover the key concepts covered in this course describe the basic features and use cases of Jupyter Notebook related to data cleaning, transformation, visualization, and machine learning name Python data analysis packages and describe their functionality describe how the NumPy package is used for data analysis describe how the Pandas package is used for data analysis work with the NumPy package functionalities for solving data analysis tasks work with the Pandas package functionalities for solving data analysis tasks outline how the Matplotlib package is used for data analytics and visualization outline how Seaborn and Bokeh packages are used for data analysis work with Matplotlib, Seaborn, and Bokeh packages to solve data analysis tasks specify how the scikit-learn package is used for classification, regression, clustering, and other tasks work with Python toolkits to tackle a real-world data analysis problem summarize the key concepts covered in this course