Simplifying Regression and Classification with Estimators

Machine Learning
  • 7 Videos | 38m 19s
  • Includes Assessment
  • Earns a Badge
Likes 9 Likes 9
This 6-video course focuses on understanding Google's TensorFlow estimators, and showing learners how they simplify the task of building simple linear and logistic regression models for machine learning solutions. As a prerequisite, learners should have a basic understanding of ML (machine learning), and basic experience programming in Python. Though not required, familiarity with the Scikit-learn library and the Keras API will simplify the labs part of this course. First, you will learn how TensorFlow estimators abstract many of the details in creating a neural network, and you will then learn that you no longer need to define the type of neural network model, nor will you need to add definitions to layer. When using an estimator, learners only need to feed in training and validation data. In the course labs, you will build both a linear regression model and a classifier by using TensorFlow estimators. Finally, you will learn how to evaluate your model using the prebuilt methods available in the estimator.  

WHAT YOU WILL LEARN

  • describe the role of estimators in speeding up the development of standard regression and classification models
    prepare a dataset to be used to train and validate a linear regression estimator
    use the estimator's methods to train and evaluate the model and visualize its performance using Matplotlib
  • transform a dataset so that it can be used to train and validate a linear classifier estimator
    use input functions to pass training and validation data to an estimator and evaluate its performance on
    utilize TensorFlow estimators with linera regression models

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 29s
    UP NEXT
  • Playable
    2. 
    Introducing Estimators
    4m 37s
  • Locked
    3. 
    Preparing Data for a Linear Regressor Estimator
    6m 58s
  • Locked
    4. 
    Training and Evaluating a Regressor Estimator
    8m
  • Locked
    5. 
    Preparing Data for a Linear Classifier Estimator
    5m 2s
  • Locked
    6. 
    Training and Evaluating a Classifier Estimator
    3m 40s
  • Locked
    7. 
    Exercise: Using TensorFlow Estimators
    5m 4s

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