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

How one model's output data is fed as input into a different model

The pipeline for converting input data between different models

It converts raw data into the input layer for the model

What is the input pipeline for a machine learning model?

What is the input pipeline for a machine learning model?

It specifies particular data examples to repeat

It repeats the dataset for a specified number of epochs

It takes in a boolean specifying whether to repeat the dataset

What does the <code>repeat</code> function do for a dataset?

What does the <code>repeat</code> function do for a dataset?

It converts a categorical feature into usable numeric data

It denotes which features are categorical

It is used to remove features that are not necessary in the data

It is used to combine multiple features into one

What is the main purpose of an indicator column?

What is the main purpose of an indicator column?

Which of the following is not a feature field for <code>tf.train.Feature</code>?

Which of the following is not a feature field for <code>tf.train.Feature</code>?

What you'll learn in this course

Data Pipeline

Model Execution

Quiz