Machine Learning: The Art of Digging Data | Data Science Online Course by Udemy

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Master Machine Learning from scratch using Python and Scikit-Learn. Code templates included.

An excellent training about Data Science

Machine Learning: The Art of Digging Data

Are you ready to start your path to becoming a Data Scientist! Are you Interested in the field of Machine Learning?Then this course is for you! This course has been designed by two master degree students who are specialized in Data Science and Machine Learning and having 2 years of experience in IT industry so that we can share our knowledge and experience to help you learn complex theory, algorithms and coding libraries in a layman’s way. This course has been design in such a way that anyone who has basic knowledge of math can understand the concepts and implement them. We will do all the coding from scratch, so that a person with zero knowledge of programming language will also be able to mastery in this field. It is structured the following way: Course IntroductionPython Tools for Machine LearningPython for Machine LearningMachine LearningJourney to SkLearnData Preprecessing and Visualization Regression Model, Tuning and ValidationClassification Model, Tuning and ValidationIntroduction to NLPThe course will not only give you grip over the concepts but it also contains some very interesting and real-life coding excercise which gives great flavour to the course. This Course will also be helpful for those who are having machine learning in their course. What knowledge & tools are required?No prior knowledge necessary. Anaconda Python distribution for codingWho should take this course?Any beginners interested to gain knowledge in Machine LearningWhat will students achieve or be able to do after taking your course?Hands-on experience of Machine Learning using Python. Basic knowledge of Python coding needed for ML. Knowledge of Machine Learning paradigms and algorithms. Knowledge of ML model creation. Mastery in Data Visualization. Knowledge on using NLP in text classification problems. Knowledge of performance metrics to increase model. Knowledge of problem-solving based on data analysis.

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