Ementorhub

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.

Skills you'll gain

Data Cleansing

Data Analysis

Predictive Modeling

Scikit Learn (Machine Learning Library)

Machine Learning

Supervised Learning

Computer Vision

Image Analysis

Data Transformation

Machine Learning Algorithms

Feature Engineering

Applied Machine Learning

There are 5 modules in this course

This course stands out for its balance between foundational concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.

Application of Machine Learning in Computer Vision

Data Features & Model Evaluation

Data Pre-Processing

Supervised Learning