Gain insights into industry-level machine learning by applying advanced TensorFlow and Python techniques. Explore efficient methods for creating scalable models, from data pipelines to deployment and inference.

AML-DLI.tar.gz

case-study

In this course, you'll level up your skills learned in the Industry Case Study and Machine Learning for Software Engineers. You'll take the modeling and data pipeline concepts and apply them to production-level classification and regression models for industry deployment, while continuing to practice the most efficient techniques for building scalable machine learning models. After this course, you will be able to complete industry-level machine learning projects, from data pipeline creation to model deployment and inference.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Deep Learning for Industry

The distribution of the input layer's data

Which of the following values does TensorBoard automatically track?

Which of the following values does TensorBoard automatically track?

It saves all the model states from each training iteration

It specifies which saved model to restore

It contains the binary saved data from the model

It specifies the variable names saved from the model

What purpose does the checkpoint file serve?

What purpose does the checkpoint file serve?

Which value is required as an input to a evaluation <code>EstimatorSpec</code>?

Which value is required as an input to a evaluation <code>EstimatorSpec</code>?

Regression cannot return negative values while classification can

Classification requires the input data to be all integers

Regression returns real values while classification returns class predictions

Classification models are always simpler than regression models

What is a difference between classification and regression?

What is a difference between classification and regression?

What you'll learn in this course

Data Pipeline

Model Execution

Quiz