1. To support our site, MoocLab may be compensated by some course providers through affiliate links.
  2. We're here to help you stay connected and progress together. Read about MoocLab's response to COVID-19 ►

    Dismiss Notice

Coursera ($) Launching into Machine Learning

Google Cloud via Coursera

  • Overview
  1. Coursera
    Platform:
    Coursera
    Provider:
    Google Cloud
    Length:
    1 week
    Effort:
    5-7 hours a week
    Language:
    English
    Cost:
    $50/month (7-day Free Trial)
    Credentials:
    Course Certificate
    Part of:
    Machine Learning with TensorFlow on Google Cloud Platform Specialization
    Overview
    Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

    Course Objectives:
    Identify why deep learning is currently popular
    Optimize and evaluate models using loss functions and performance metrics
    Mitigate common problems that arise in machine learning
    Create repeatable and scalable training, evaluation, and test datasets

    Syllabus
    Introduction
    In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.

    Practical ML
    In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.

    Optimization
    In this module we will walk you through how to optimize your ML models.

    Generalization and Sampling
    Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world.

    Summary

    Taught by
    Google Cloud Training

Share This Page



  1. This site uses cookies to help personalise content, tailor your experience and to keep you logged in if you register.
    By continuing to use this site, you are consenting to our use of cookies.
    Dismiss Notice