Machine Learning Mastery (Integrated Theory+Practical HW)

Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset. Students will learn the theories, techniques, and tools they need to deal with various datasets. We will start with Regression, one of the basic models, and progress as we evaluate and assessing different models. We will start from the initial stages of data science and advance to higher levels where students can write their own algorithm from scratch to build a model. We will see end to end and work with practical datasets at the end of each module. Students will be issued with tutorials and explanation of all the exercises to help you learn faster and enable you to link theory using hands on exercises. This course teaches advanced theory including some mathematics with practical exercises to promote deeper understanding.Learning OutcomesAt the end of the course the students will:Have an in-depth understanding of the concepts of Machine LearningBe able to grasp, understand, and write machine learning code from scratch Use Builtin Libraries available to build machine learning modelsBe able to analyze, build, and assess models on any datasetBe able to interpret and understand the black box behind modelUnderstand the applications of data science by exhibiting the ability to work on different datasets and interpre