Leveraging Online Resources for Python Analytics

This course introduces important resources for data science, including visualization libraries, deep learning frameworks, and cloud-based environments. It also explores BigML and Google Colab—powerful resources for building and sharing analytics.
Course info
Rating
(13)
Level
Advanced
Updated
Nov 1, 2019
Duration
2h 11m
Table of contents
Description
Course info
Rating
(13)
Level
Advanced
Updated
Nov 1, 2019
Duration
2h 11m
Description

As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming.

In this course, Leveraging Online Resources for Python Analytics, you will gain the ability to find resources that can help you to correctly frame and solve your problem. First, you will survey some of the important visualization libraries, machine learning and deep learning frameworks, and cloud-based solutions out there.

Next, you will discover the benefits of using a tool like BigML, which is a platform for building ML models that abstracts away much of the underlying complexity. Democratization of ML is an important trend today, and technologies like BigML are at the forefront of that trend. You will see, for instance, how BigML seamlessly integrates visualizations known as partial dependency plots, which combine the results of large numbers of ML predictions into an easily understandable form so that you can understand exactly what your ML model is doing.

Finally, you will round out your knowledge by working with Google Colab, a free web-based way to build models. The models are hosted in Jupyter notebooks that reside on Google Drive and run on virtual machines in the cloud.

When you’re finished with this course, you will have the skills and knowledge to quickly and efficiently identify valuable online resources and libraries that will help you on your journey as a data science practitioner.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi. My name is Janani Ravi, and welcome to this course on Leveraging Online Resources for Python Analytics. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. As data science and data analytics become even more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. In this course, you will gain the ability to find resources that can help you to correctly frame and solve your problem. First, you will survey some of the important visualization libraries, machine learning and deep learning frameworks, and cloud-based solutions out there. Next, you will discover the benefits of using a tool like BigML, which is a platform for building ML models that abstracts away much of the underlying complexity. You will see, for instance, how BigML seamlessly integrates visualizations, known as partial dependence plots, which combine the results of large numbers of ML predictions into an easily understandable form so that you can understand exactly what your model is doing. Finally, you will round out your knowledge by working with Google Colab, a free web-based way to build models. The models are hosted in Jupyter Notebooks that reside on Google Drive and run on virtual machines on the cloud. When you're finished with this course, you will have the skills and knowledge to quickly and efficiently identify valuable online resources and libraries that will help ease your journey as a data science practitioner.