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

<h4>A. How machine learning is used in industry</h4>

Machine learning has become one of the most important tools used in the tech industry. It powers everything from self-driving cars to virtual assistants. Nearly every company either has machine learning in their tech stack or is planning to incorporate machine learning in the near future.

Many people who have taken a college course or an online course in machine learning are surprised at how different the machine learning process is in industry. Very seldom in industry will people need to know much of the theory or mathematics behind machine learning; rather, the main focus is in applying existing algorithms and models to new problems and datasets.

Furthermore, the majority of work done by machine learning engineers in industry has nothing to do with actually building out machine learning models. Rather, most of the time spent on machine learning projects is in analyzing datasets, creating a data pipeline, and interpreting model results.


<h4>B. What this course will provide</h4>

After taking this course, you’ll be able to take on the same types of projects done by industry machine learning engineers. Specifically, you will be able to:

* Process and analyze datasets.
* Create an efficient data pipeline for feeding input data into machine learning models.
* Develop robust machine learning models for both classification and regression tasks.
* Interpret model results with respect to the project domain.

# A. How machine learning is used in industry

Machine learning has become one of the most important tools used in the tech industry. It powers everything from self-driving cars to virtual assistants. Nearly every company either has machine learning in their tech stack or is planning to incorporate machine learning in the near future.

Many people who have taken a college course or an online course in machine learning are surprised at how different the machine learning process is in industry. Very seldom in industry will people need to know much of the theory or mathematics behind machine learning; rather, the main focus is in applying existing algorithms and models to new problems and datasets.

Furthermore, the majority of work done by machine learning engineers in industry has nothing to do with actually building out machine learning models. Rather, most of the time spent on machine learning projects is in analyzing datasets, creating a data pipeline, and interpreting model results.


# B. What this course will provide

After taking this course, you’ll be able to take on the same types of projects done by industry machine learning engineers. Specifically, you will be able to:

* Process and analyze datasets.
* Create an efficient data pipeline for feeding input data into machine learning models.
* Develop robust machine learning models for both classification and regression tasks.
* Interpret model results with respect to the project domain.

What you'll learn in this course

Data Pipeline

Model Execution

Overview

A. How machine learning is used in industry

B. What this course will provide