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

In this section you will be learning about the model execution process in TensorFlow. This involves model training, evaluation, and making predictions. The techniques and APIs introduced in the upcoming chapters are used by top industry professionals to easily create organized and efficient machine learning models.
The first model you create is going to be a classification model. Classification models take data observations and label them with a certain category. For example, classification models can used for tasks like fraud detection, where something is categorized as either fraud or legitimate.
The second model you create is going to be a regression model. Regression models take data observations and output a number. which which can be used for predicting sales with this course's supermarket dataset.

An overview of the development process for machine learning models.

What you'll learn in this course

Data Pipeline

Model Execution

Introduction