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Updated in [June 30th, 2023]

This course, Machine Learning Foundations: A Case Study Approach, provides an introduction to the field of machine learning. It covers the core principles of machine learning, and provides hands-on experience with practical case studies. Students will learn how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, students will be able to apply machine learning methods in a wide range of domains. The course also covers the core differences in analyses enabled by regression, classification, and clustering, and how to select the appropriate machine learning task for a potential application. By the end of the course, students will be able to identify potential applications of machine learning in practice, apply regression, classification, clustering, retrieval, recommender systems, and deep learning, represent data as features to serve as input to machine learning models, assess the model quality in terms of relevant error metrics for each task, utilize a dataset to fit a model to analyze new data, build an end-to-end application that uses machine learning at its core, and implement these techniques in Python.

[Applications]
Upon completion of this course, learners will be able to apply machine learning methods to a wide range of domains. They will be able to identify potential applications of machine learning, select the appropriate machine learning task for a potential application, and assess the model quality in terms of relevant error metrics for each task. Learners will also be able to represent data as features to serve as input to machine learning models, build an end-to-end application that uses machine learning at its core, and implement these techniques in Python.

[Career Paths]
One job position path that is recommended for learners of this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms to solve real-world problems. They must have a strong understanding of the fundamentals of machine learning, such as supervised and unsupervised learning, deep learning, and natural language processing. They must also be proficient in programming languages such as Python, R, and Java, and be able to use various machine learning frameworks such as TensorFlow, Keras, and Scikit-Learn.

The development trend of Machine Learning Engineer is very positive. With the increasing demand for machine learning applications in various industries, the demand for Machine Learning Engineers is also increasing. Companies are looking for Machine Learning Engineers who can develop and deploy machine learning models and algorithms to solve real-world problems. As the technology advances, Machine Learning Engineers will need to stay up to date with the latest trends and technologies in order to remain competitive.

[Education Paths]
The recommended educational path for learners of this course is to pursue a degree in Machine Learning. This degree typically involves taking courses in mathematics, computer science, and statistics, as well as courses in machine learning and artificial intelligence. Students will learn the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. They will also learn how to apply these techniques to real-world problems, such as image recognition, natural language processing, and robotics. Additionally, students will gain experience in programming languages such as Python and R, as well as in data analysis and visualization.

The development trend of machine learning degrees is to focus on the practical application of machine learning techniques. This includes courses in data engineering, data science, and software engineering, as well as courses in machine learning and artificial intelligence. Additionally, courses in cloud computing, big data, and distributed systems are becoming increasingly important. As machine learning becomes more widely used, the demand for professionals with the skills to develop and deploy machine learning applications is growing.