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

# Chapter Goals:
- Learn how to use a neural network for regression
- Set up the training architecture for a regression model

# A. Classification vs. regression
The model we've been using so far is built for classification. This means it classifies input data into one of many different classes.

While classification is one of the most important tasks that can be done by a neural network, another common application of neural networks is *regression*. Rather than assigning the input data to a particular class, a regression model will instead output a real number value based on the input data. Regression tasks can range from predicting stock prices to predicting the future height of a child.

Classification models obtain probabilities and class predictions from the calculated logits. A regression model can just return the logits, since the logits represent the real-valued output of the model's layers.

Understand the difference between regression and classification models.

What you'll learn in this course

Data Pipeline

Model Execution

Regression

Chapter Goals:

A. Classification vs. regression