❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

This course provides an overview of practical machine learning. Students will learn the basics of building and applying prediction functions, with an emphasis on practical applications. Topics covered include training and test sets, overfitting, error rates, model-based and algorithmic machine learning methods such as regression, classification trees, Naive Bayes, and random forests. The course will also cover the complete process of building prediction functions, from data collection and feature creation to algorithms and evaluation. By the end of the course, students will have a better understanding of the fundamentals of machine learning and be able to apply them to real-world problems.

[Applications]
The application of this course can be seen in a variety of fields. It can be used to create predictive models for marketing, finance, healthcare, and other industries. It can also be used to create models for predicting customer behavior, predicting stock prices, and predicting disease outbreaks. Additionally, the course can be used to create models for natural language processing, image recognition, and other machine learning tasks. Finally, the course can be used to create models for autonomous vehicles, robotics, and other applications.

[Career Path]
The career path recommended to learners of this course is that of 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 are responsible for designing, building, and maintaining machine learning systems, as well as for developing and testing new algorithms. They must be able to work with large datasets and have a strong understanding of the underlying mathematics and statistics.

The development trend for Machine Learning Engineers is very positive. As the demand for data-driven decision making increases, so does the need for Machine Learning Engineers. Companies are increasingly looking for Machine Learning Engineers to help them make better decisions and to automate processes. Additionally, the development of new technologies such as artificial intelligence and deep learning are creating new opportunities for Machine Learning Engineers.

[Education Path]
The recommended educational path for learners interested in Practical Machine Learning is a Bachelor's degree in Computer Science or a related field. This degree will provide a strong foundation in the fundamentals of computer science, including programming, data structures, algorithms, and software engineering. Additionally, the degree will provide a comprehensive overview of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning.

The development trend for this degree is to focus on the practical application of machine learning. This includes the use of machine learning to solve real-world problems, such as predicting customer behavior, analyzing medical data, and forecasting stock prices. Additionally, the degree will focus on the development of new algorithms and techniques to improve the accuracy and efficiency of machine learning models. Finally, the degree will emphasize the use of big data and cloud computing to enable the development of more powerful machine learning models.