Applied Machine Learning – Techniques and Applications
Part of the Certificate in Applied Machine Learning
Start anytime. Learn at your own pace.
Build essential skills in data preparation, model training, and results evaluation.
Artificial Intelligence
Online Self-Paced
20 hours
2 CEUs
$500
Take as a standalone course or as part of the
Certificate in Applied Machine Learning
Instructor: Dr. Erhan Guven
Curriculum designed and delivered by Johns Hopkins faculty
LIVE monthly seminars and office hours
Engaging learning including video walkthroughs and hands-on activities
Satisfaction guaranteed. Explore the course with no risk.
In machine learning, even simple models with well-prepared data can outperform complex algorithms trained on poorly processed inputs. Mastering the fundamentals is essential.
With step-by-step video guidance from instructor Dr. Erhan Guven, the first course in the Certificate in Applied Machine Learning puts you straight into building working classification systems while ensuring you have the essential knowledge of data preparation and feature engineering—the keys to ML success. The videos are complemented by projects, insightful readings and quizzes to test your comprehension.
Dr. Guven’s Jupyter notebooks will keep you on the right track, helping you understand the machine learning workflow while challenging you to implement key components yourself. You’ll develop practical coding skills (and confidence) as you progress from data preparation through model training to evaluation and refinement.
With scikit-learn, pandas, and real-world datasets you’ll implement your first Support Vector Machines, Naive Bayes classifiers and text categorizing systems. Then you’ll make them more accurate and more predictive.
By the conclusion of this course, you’ll be able to implement the complete machine learning workflow and have an excellent foundation to build upon.
Prerequisites
You should have basic Python programming skills and comfort with high school-level math. The focus is on practical implementation rather than mathematical theory. If you can write simple Python functions and aren’t intimidated by working with data in tables, you’re ready to go.
No Risk: Satisfaction Guaranteed
Feel confident in your learning journey! If the course content is too advanced, not advanced enough, or simply doesn’t meet your expectations, we’ve got you covered with our money-back guarantee. Just contact our team within 7 days from purchase to receive a full refund—no questions asked.
Meet Your Instructor
Dr. Erhan Guven
Johns Hopkins University, Johns Hopkins Applied Physics Laboratory
Dr. Guven is an AI scientist at Johns Hopkins University Applied Physics Laboratory and assistant program manager in Johns Hopkins Engineering’s #1 ranked online master’s programs in AI and data science. His research spans a broad spectrum of machine learning applications, including large language models, financial systems cybersecurity, NLP, and bioinformatics. In his spare time, he enjoys gardening, beekeeping, building computers and playing Defense of the Ancients.
Dr. Guven is Here to Help!
Questions about course content? Looking compare model results or get feedback? Stop by monthly Zoom office hours to talk with Erhan and fellow students.
Projects You’ll Build (With Expert Guidance)
With ready-to-use Jupyter notebooks and working code examples, Dr. Guven will walk you through creating…
- The Titanic Survival Predictor (a classic ML introduction)
Join over a million data scientists who’ve started their machine learning journey by transforming passenger information into survival predictions. Post your results to the Kaggle leaderboard and get immediate feedback. - Handwritten Digit Recognizer
A classification system using Support Vector Machines to automatically identify handwritten digits from the MNIST dataset. - Cancer Reoccurrence Predictor
Create a text classifier using TF-IDF vectorization to identify 20 different programming languages from Stack Overflow posts, training linear SVM models on a dataset of 40,000 code snippets. - Stack Overflow Language Classifier
Create a text classifier using TF-IDF vectorization to identify 20 different programming languages from Stack Overflow posts, training linear SVM models on a dataset of 40,000 code snippets. - Graduate Admissions Forecaster
Develop regression models to predict graduate school admission probability, implementing data transformation and normalization to improve prediction accuracy.
Save $300 and Earn the Full Certificate
Applied Machine Learning – Techniques and Applications is one of 3 courses in the full
The image is for illustrative purposes only. Actual certificate design subject to change,
Complete this course as well as:
and the capstone project to earn your Johns Hopkins Certificate of Achievement.
Say $300 when you purchase the full Certificate in Applied Machine Learning instead of paying for each individually.
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Course Delivery and Support
The courses are delivered entirely online through the industry-leading Canvas Learning Management System. This system is supported by the same instructional design team behind Johns Hopkins’ renowned Engineering for Professionals program, which serves thousands of online graduate students each year. Upon registration, you will receive an email with instructions to create your Hopkins Canvas account and access the videos, readings, files and quizzes.
Applied Machine Learning – Techniques and Applications
Artificial Intelligence
Online Self-Paced
20 hours
2 CEUs
$500
Take as a standalone course or as part of the
Certificate in Applied Machine Learning
No Risk: 7-Day Money-Back Guarantee