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 evaluate a regression model
- Use the `EstimatorSpec` object to organize results from training, evaluation, and prediction

## A. Regression evaluation
Unlike classification models, we can't use the accuracy metric to evaluate regression. Since the output of regression models is a real number, rather than a class prediction, there's no definite way to say what is "correct" or "incorrect".

However, we can tell how good a model output is based on its distance from the corresponding label. For example, if the label for some data observation was 0.2 and the model returned 0.199, the prediction is excellent. On the other hand, if the model returned 812.11, the prediction is likely very poor.

The metric that corresponds to this idea is [mean squared error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error). The MSE is very similar to the L2-norm, in that both are based on the squared difference between labels and predictions.

In TensorFlow, we obtain the MSE metric using [`tf.compat.v1.metrics.mean_squared_error`](https://www.tensorflow.org/api_docs/python/tf/metrics/mean_squared_error). The function takes in the labels and model predictions as its two required arguments.

# Chapter Goals:
- Learn how to evaluate a regression model
- Use the `EstimatorSpec` object to organize results from training, evaluation, and prediction

# A. Regression evaluation
Unlike classification models, we can't use the accuracy metric to evaluate regression. Since the output of regression models is a real number, rather than a class prediction, there's no definite way to say what is "correct" or "incorrect".

However, we can tell how good a model output is based on its distance from the corresponding label. For example, if the label for some data observation was 0.2 and the model returned 0.199, the prediction is excellent. On the other hand, if the model returned 812.11, the prediction is likely very poor.

The metric that corresponds to this idea is [mean squared error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error). The MSE is very similar to the L2-norm, in that both are based on the squared difference between labels and predictions.

In TensorFlow, we obtain the MSE metric using [`tf.compat.v1.metrics.mean_squared_error`](https://www.tensorflow.org/api_docs/python/tf/metrics/mean_squared_error). The function takes in the labels and model predictions as its two required arguments.

What you'll learn in this course

Data Pipeline

Model Execution

EstimatorSpec

Chapter Goals:

A. Regression evaluation