Columbia Engineering Executive Education’s Applied Machine Learning course aims to prepare aspiring data scientists and professionals with a comprehensive understanding of both supervised and unsupervised learning approaches.
But is this course the best fit for your machine learning journey?
In this in-depth Columbia Applied Machine Learning review, we’ll navigate through the course’s key features, curriculum, faculty, highlights, as well as its pros and cons to help you make an informed decision about whether this is the right direction for your career advancement.
So let’s dive right in.
What is the Columbia Applied Machine Learning Course?
The Applied Machine Learning course is a 5-month online learning experience offered by Columbia Engineering Executive Education.
It focuses on both supervised and unsupervised machine learning approaches, with Python being the core programming language for the course.
The course is designed for those who wish to lead or implement a machine learning project or integrate machine learning capabilities into their software applications.
It’s a programming-oriented course requiring an intermediate knowledge of Python, statistics, calculus, linear algebra, and probability.
Fear not, the initial modules are devoted to Python for Data Analytics, ensuring you’re well-prepared for the learning journey.
Key Features and Highlights
Let’s check out some of the items that make this applied machine learning Columbia course a real contender:
The course comprises a mammoth 240+ faculty video lectures, 45 quizzes/assignments, 18 moderated discussion boards, and 20+ Q&A sessions with course leaders.
It also includes 12 hands-on application projects to put your theoretical learning into practice. The course comes complete with live online teaching, making it a truly interactive and engaging learning experience.
The curriculum is divided into two parts: Python for Data Analytics and Applied Machine Learning.
The Python modules are delivered by Emeritus, a renowned global leader in online education, ensuring you start your journey with a solid foundation.
The subsequent Machine Learning modules are delivered by Columbia Engineering.
Curriculum and Modules
The Applied Machine Learning course from Columbia Engineering Executive Education is a comprehensive curriculum designed to cater to all aspects of machine learning. The modules are carefully organized into the following main areas:
Python for Data Analytics: Brush up on Python basics and explore various Python libraries like Pandas, Numpy, and Matplotlib, essential for data manipulation and visualization.
Supervised Learning Techniques: Delve into the realm of supervised learning, exploring linear regression, logistic regression, decision trees, random forests, and boosting techniques.
Unsupervised Learning Techniques: Learn about clustering techniques, including K-Means and hierarchical clustering, and Principal Component Analysis (PCA).
Deep Learning: Deep dive into neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks.
Natural Language Processing (NLP): Understand the basics of text processing and sentiment analysis and familiarize yourself with techniques like Word2Vec.
Application Projects: Apply your learning in 12 different projects, from building a movie recommendation system to predicting house prices, human activity, and detecting credit card fraud.
The Faculty
Leading the applied deep learning Columbia course is Dr. John W. Paisley, Associate Professor in Electrical Engineering at Columbia University.
Dr. Paisley has a Ph.D. from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley.
His research focuses on developing models for large-scale text and image processing applications. Under his guidance, the course gains a practical and research-oriented approach, which is a significant advantage.
Pros
Just a few reasons why we think the Columbia Applied Machine Learning program is worth a look:
- A holistic curriculum that covers all essential aspects of machine learning, providing a well-rounded learning experience.
- The course is designed and taught by Columbia Engineering faculty, ensuring high-quality education.
- The course offers a perfect blend of theory and practice with 12 hands-on application projects.
- Interactive learning through live Q&A sessions and discussion boards enhances the online learning experience.
- Access to course materials for two years, allowing you to learn at your own pace.
Cons
- The course fee could be a hurdle for some learners, although early bird discounts are available.
- A background in Python, statistics, calculus, linear algebra, and probability is required, which might be challenging for beginners.
The Learning Experience
Learning with the Applied Machine Learning course was a journey of depth and discovery.
From building regression models to developing unsupervised models to extract hidden patterns from extensive data, the course had me constantly engaged and challenged. The curriculum’s progression was logical and methodical, starting with the basics and then diving deep into the complex aspects of machine learning.
The Python for Data Analytics modules were enriching, providing a solid foundation for the journey ahead.
The live Q&A sessions and moderated discussion boards offered an interactive learning experience, reminiscent of a traditional classroom setting, but with the convenience and flexibility of online learning.
Pricing
The Applied Machine Learning course is priced at $2,350.
While this might appear as a substantial investment, the value derived from the extensive curriculum, live teaching sessions, practical projects, and a certificate from Columbia Engineering far outweighs the cost.
There are normally early-bird discounts available so make sure you head to their website to check if there are any offers available such as a 15% discount.
What is the Emeritus Online Learning Platform?
Emeritus, known for its global collaboration with top-tier universities, offers an online platform that ensures an interactive and immersive learning experience.
The Applied Machine Learning course leverages this platform to provide a seamless learning experience, replicating a real-time classroom environment that allows learners to interact with faculty and peers.
Thanks to Emeritus, this rigorous machine learning course is accessible to professionals worldwide, seeking to enhance their skills and knowledge.
You can check out a whole range of other courses offered through Emeritus by other world renowned universities.
Application Projects
One of the standout features of the Applied Machine Learning course from Columbia Engineering Executive Education is the diverse range of application projects included in the curriculum.
These projects are designed to give students hands-on experience with real-world challenges, allowing them to apply the theoretical knowledge they’ve learned in a practical setting.
In total, the course features 12 application projects. These projects range from developing a movie recommendation system to predicting real estate prices.
Students also get to work on human activity recognition using smartphone data and detecting credit card fraud. This experiential learning allows students to work on diverse machine learning applications, contributing to a portfolio that showcases their competence and skill in this domain.
What I Like About the Applied Machine Learning Course
This course shines in several areas.
The comprehensive curriculum, which covers everything from Python basics to deep learning and natural language processing, provides a well-rounded learning experience. The course’s structure, combining theory with hands-on projects, ensures that students not only understand the concepts but also know how to apply them practically.
The fact that the course is designed and delivered by Columbia Engineering faculty adds an extra layer of credibility and trust. The opportunity to engage with faculty through live Q&A sessions and discussion boards further enriches the learning experience.
Finally, the two-year access to course materials makes this course particularly appealing. This flexibility allows learners to study at their own pace and revisit the material whenever needed.
What Sets This Course Apart?
The Columbia Applied Machine Learning course’s distinguishing feature is its emphasis on practical application.
With 12 application projects woven into the curriculum, students get the opportunity to apply what they’ve learned and tackle real-world challenges. This experiential learning approach helps students internalize the concepts, develop problem-solving skills, and become competent machine learning practitioners.
Moreover, the course’s faculty members are renowned professionals from Columbia Engineering, ensuring top-quality instruction. The interactive learning environment Emeritus fosters, with its live Q&A sessions and discussion boards, makes this online course feel just as engaging as an in-person class.
Who Should Take This Course?
The Applied Machine Learning course is designed for professionals looking to delve into the world of machine learning. This includes software engineers, data analysts, business analysts, statisticians, and anyone interested in leveraging machine learning to extract insights from data and make informed decisions.
While the course covers Python basics, a preliminary understanding of Python, statistics, calculus, linear algebra, and probability is beneficial. This course is ideal for those who are serious about mastering machine learning and are willing to commit to a rigorous learning journey.
Bottom Line
In a nutshell, the Applied Machine Learning course from Columbia Engineering Executive Education is an excellent investment for anyone looking to upskill in machine learning.
The course’s comprehensive curriculum, hands-on projects, live teaching sessions, and the certificate from a renowned institution like Columbia Engineering make it a high-value offering in the field.
The price tag might seem hefty initially, but considering the quality of instruction, the extensive learning material, and the practical experience gained, it’s a worthy investment. The course equips students with a deep understanding of machine learning, preparing them for a thriving career in the data-driven world.
Justin is a full-time data leadership professional and a part-time blogger.
When he’s not writing articles for Data Driven Daily, Justin is a Head of Data Strategy at a large financial institution.
He has over 12 years’ experience in Banking and Financial Services, during which he has led large data engineering and business intelligence teams, managed cloud migration programs, and spearheaded regulatory change initiatives.