You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.

You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.

Learning the theoretical background for data science or machine learning can be a daunting experience, as it involves multiple fields of mathematics and a long list of online resources. In this piece, my goal is to suggest resources to build the mathematical background necessary to get up and running in data science practical/research work. These suggestions are derived from my own experience in the data science field and following up with the latest resources suggested by the community. However, suppose you are a beginner in machine learning and looking to get a job in the industry. In that case, I don't recommend studying all the math before starting to do actual practical work.

We provide a comprehensive review of classical algorithms for compressive sensing of images, focused on Total variation methods, with a view to application in LiDAR systems. Our primary focus is providing a full review for beginners in the field, as well as simulating the kind of noise found in real LiDAR systems. To this end, we provide an overview of the theoretical background, a brief discussion of various considerations that come in to play in compressive sensing, and a standardized comparison of off-the-shelf methods, intended as a quick-start guide to choosing algorithms for compressive sensing applications.

Learning the theoretical background for data science or machine learning can be a daunting experience, as it involves multiple fields of mathematics, and a long list of online resources. In this piece, my goal is to suggest resources to build the mathematical background necessary to get up and running in data science practical/research work. These suggestions are derived from my own experience in the data science field, and following up with the latest resources suggested by the community. However, if you are a beginner in machine learning and looking to get a job in industry, I don't recommend studying all the math before starting to do actual practical work, this bottom up approach is counter-productive and you'll get discouraged, as you started with the theory (dull?) before the practice (fun!). My advice is to do it the other way around (top down approach), learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real world projects, use libraries documentations and YouTube/Medium tutorials.