AWS Certified Machine Learning: Jupyter Notebook & Python

Amazon Web Services 2021
  • 13 Videos | 38m 50s
  • Includes Assessment
  • Earns a Badge
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 10s
    UP NEXT
  • Playable
    2. 
    What Is Jupyter Notebook?
    2m 35s
  • Locked
    3. 
    Python for Data Science
    2m 27s
  • Locked
    4. 
    Python Packages - NumPy
    1m 37s
  • Locked
    5. 
    Python Packages - Pandas
    1m 21s
  • Locked
    6. 
    Working with NumPy Packages
    5m 37s
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    7. 
    Working with Pandas Packages
    6m 28s
  • Locked
    8. 
    Python Packages - Matplotlib
    1m 15s
  • Locked
    9. 
    Python Packages - Seaborn and Bokeh
    1m 29s
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    10. 
    Using Matplotlib, Seaborn, & Bokeh for Data Analysis
    6m 31s
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    11. 
    Machine Learning in Python with scikit-learn
    1m 48s
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    12. 
    Implementing Machine Learning Models Using Python
    5m 50s
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    13. 
    Course Summary
    43s

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