Applied Machine Learning: Techniques and Applications

Overview

Class Central Tips

The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. 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.

Syllabus

Course Introduction

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.

Application of Machine Learning in Computer Vision

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.

Data Features & Model Evaluation

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.

Data Pre-Processing

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.

Supervised Learning

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.