As an AI language model, I do not have information on specific courses offered by institutions or individuals. However, I can provide a general description of what a course on Applied Machine Learning: Techniques and Tools might cover.

Such a course would likely be designed to provide students with a practical understanding of machine learning techniques and tools and how to apply them to solve real-world problems. The course might cover a range of topics, including supervised and unsupervised learning, deep learning, data preprocessing, model evaluation, and various tools and frameworks used in machine learning.

The course might also include hands-on assignments or projects to give students practical experience applying machine learning techniques to real-world datasets. Students may be expected to work with popular machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras, and learn how to use these tools to train and evaluate models.

Overall, a course on Applied Machine Learning: Techniques and Tools would likely be ideal for anyone interested in using machine learning to solve real-world problems, including data analysts, data scientists, machine learning engineers, and software developers. By the end of the course, students should be able to understand and apply various machine learning techniques and tools, and have the skills needed to tackle machine learning projects independently.