Johns Hopkins University
Applied Machine Learning: Techniques and Applications
Johns Hopkins University

Applied Machine Learning: Techniques and Applications

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand and implement machine learning techniques for computer vision tasks, including image recognition and object detection.

  • Analyze data features and evaluate machine learning model performance using appropriate metrics and evaluation techniques.

  • Apply data pre-processing methods to clean, transform, and prepare data for effective machine learning model training.

  • Implement and optimize supervised learning algorithms for classification and regression tasks.

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

September 2024

Assessments

12 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Applied Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 5 modules in this course

Explore the practical applications of machine learning through hands-on modules covering data pre-processing, feature extraction, model evaluation, and supervised learning techniques. Delve into specialized topics such as computer vision and learn to implement and assess various machine learning models. This course combines theoretical insights with practical lab activities to equip you with essential skills in applied machine learning.

What's included

2 readings

Discover the foundational principles and practical applications of machine learning in the field of computer vision. This module covers essential concepts, including data preprocessing, dataset management, classification techniques, and model evaluation, providing a comprehensive introduction to applying machine learning to visual data.

What's included

5 videos2 readings3 assignments1 ungraded lab

Explore essential techniques in data feature analysis and model evaluation critical to effective machine learning applications. Learn to identify, preprocess, and integrate datasets from diverse sources like UCI KDD and Kaggle. Gain hands-on experience with the Weka framework for data preprocessing and classification, and understand evaluation metrics including Receiver Operating Characteristic curves. By the end of this module, you'll grasp the nuances of model overfitting and strategies to optimize model performance.

What's included

7 videos2 readings3 assignments1 ungraded lab

Master the essential techniques of data pre-processing to enhance machine learning model performance. This module covers the foundational aspects of data cleaning, various data formats, and processing methods. You'll delve into advanced topics like discretization, data transformation, and reduction techniques. By the end of this module, you'll be adept at engineering data features, applying feature selection, and refining datasets for optimal machine learning outcomes.

What's included

5 videos1 reading3 assignments1 ungraded lab

Delve into the core principles and mathematical foundations of supervised learning algorithms. This module covers essential techniques, including the Perceptron algorithm, Naive Bayes classifier, and Linear Regression methods. You'll gain practical experience implementing and visualizing these algorithms, and explore how classifier decision boundaries shift with parameter changes. Additionally, learn to apply text classification using real-world datasets for hands-on understanding of supervised learning applications.

What's included

6 videos2 readings3 assignments1 programming assignment

Instructor

Erhan Guven
Johns Hopkins University
3 Courses14 learners

Offered by

Recommended if you're interested in Machine Learning

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Machine Learning? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions