Machine Learning and Deep Learning

Machine learning and deep learning are the driving forces behind modern AI advancements. In this course, you expand on your foundational machine learning knowledge, diving deeper into advanced techniques and deep learning architectures. Explore neural networks, learning how they process vast amounts of data to identify patterns and make predictions. Using Apache Spark for distributed data processing and TensorFlow for building and training deep learning models, you gain hands-on experience with real-world AI applications. This course is ideal for data scientists and developers looking to integrate deep learning into their work.

Prerequisites:

Experience working with machine learning algorithms
Python programming experience
Prior coursework in calculus and linear algebra is helpful

Learner Outcomes

On successful completion, participants will be able to:

Create and validate machine learning models
Use machine learning libraries provided by Spark/MLLib and TensorFlow in Python
Explain neural networks and deep learning concepts
Conduct data processing and feature engineering in Spark
Optimize and scaling machine learning pipelines
Understand CNN and RNN architecture and applications
Effectively utilize supervised and unsupervised algorithms

Sections

Summer 2025 enrollment opens on March 17!

This course applies to the following programs:

Learner Outcomes

Sections

Program in Data Science

Databases & Big Data

Data Science

Data Science Capstone

Notes

English Language Proficiency Requirements

Machine Learning and Deep Learning

Learner Outcomes

Sections

Thank you for your interest in this course!

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Program in Data Science

Databases & Big Data

Data Science

Data Science Capstone