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This course will provide the technical knowledge you need to get started with applying Machine Learning (ML) to solve your problem efficiently and at scale. We start from the data stage, move onto ML concepts, tying them back to example use cases and their evaluation, and also cover planning and scaling strategies that help you get your solution out into the world. Beyond that, the course also covers steps that help you continuously maintain and improve your solution pipeline, throughout its lifecycle. There could be parts of this course that the learner may be aware of already, but as someone who does this day in and out, I have tried to include scenarios, challenges, steps and the outlook to face even well known topics with more confidence than before, and put them together in a well-ordered flow. This might come in handy to someone preparing for an interview in this field. As someone who has learnt courses on the go during commute or other times, and having realised the time saving value, I have made the course's audio content substantially context rich for those who prefer consuming it through audio. It does have the video component as well, for visual learners.This course can act as a well organised end-to-end guidebook to integrate Data Science and Machine Learning knowledge across the board into the everyday work of a Business Leader, Product Manager, Software Developer, Researcher, Analyst or Data Scientist, by being realistic and holistic. The learner can use this as a framework and mindset, that will enable them to think objectively and comprehensively at all stages of data and ML adaptation and application, thereby increasing its success rate.

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