Discover the power of machine learning with the "Machine Learning Foundations: A Case Study Approach" course. Through a series of practical case studies, you will gain hands-on experience in predictive analysis, sentiment analysis, document retrieval, product recommendation, and image searching.
Throughout the course, you will delve into the core ways in which machine learning can revolutionize business processes. From regression and classification to deep learning and recommender systems, you will explore the key concepts and applications of machine learning.
By delving into the black box of machine learning, you will understand the tasks of interest, match them to machine learning tools, and assess the quality of the output. The course will equip you with the ability to identify potential applications of machine learning, select the appropriate machine learning tasks, represent data as features for input, and build end-to-end applications using Python.
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Embark on a comprehensive journey through practical case studies to gain hands-on experience in machine learning concepts, including regression, classification, clustering, retrieval, recommender systems, and deep learning.
Welcome to the course and specialization, where you will learn about the core ways in which machine learning can improve business processes. Gain insights into regression, classification, clustering, retrieval, recommender systems, and deep learning through a series of practical case studies.
Delve into the world of regression by exploring a case study on predicting house prices. Understand linear regression, overfitting, and the evaluation of error metrics. Apply your knowledge to analyze and predict house prices based on various features.
Explore the analysis of sentiment from user reviews in the classification module. Learn about linear classifiers, decision boundaries, and training and evaluating a classifier. Discover how to apply these techniques to differentiate between positive and negative sentiment.
Dive into the world of clustering and similarity by studying a case study on document retrieval. Understand the importance of word count representation, tf-idf vectors, and nearest neighbor search. Apply your knowledge to retrieve similar documents and understand unsupervised learning tasks.
Discover the world of recommender systems and learn how to build personalized recommendation systems. Explore collaborative filtering, matrix completion tasks, and precision-recall curves. Apply your knowledge to build and evaluate recommender systems for products and songs.
Embark on a deep learning journey by exploring a case study on searching for images. Learn about neural networks, deep features, and their application to computer vision. Apply your knowledge to train and evaluate classifiers and retrieve similar images using deep features.
Conclude your learning journey with insights into deploying an ML service, open challenges in ML, and the future of machine learning. Gain a comprehensive understanding of where the field of machine learning is heading and its potential impact on various industries.
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