APLCS.tar.gz

case-study

In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will be able to take on industry-level machine learning projects, from data analysis to creating efficient models and providing results and insights.

The code for this course is built around the TensorFlow framework, which is one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

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: Industry Case Study with TensorFlow

Training set is used for model training, evaluation set is used to validate model performance

Evaluation set is used for additional training after training with the regular training set

Training and evaluation sets are used to train different models in parallel

Evalution set is used to train a smaller version of the same model from the training set

What's the purpose of splitting a dataset into training and evaluation sets?

What’s the purpose of splitting a dataset into training and evaluation sets?

It can store more different datatypes compared to other file formats

It is the only file format that's allowed in the input pipeline for TensorFlow

TFRecords files are smaller than just storing the data in JSON or CSV files

TFRecords files provide a faster and more efficient input pipeline for the model

What's the main benefit to converting the dataset into a TFRecords file?

What’s the main benefit to converting the dataset into a TFRecords file?

Random shuffling of the elements in the dataset

Which of the following is not a configuration used for our project's TensorFlow dataset?

Which of the following is not a configuration used for our project’s TensorFlow dataset?

It converts a string feature into a binary vector that represents categorical data

It is essentially a weighted indicator feature column, with weights learned by the model

It provides a real number vector representation of a categorical dataset feature

What's the purpose of an embedding feature column?

What’s the purpose of an embedding feature column?

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Quiz