Data Cleaning Techniques in Data Science & Machine Learning

One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision making Improve the efficiency Increase productivity Remove the errors and inconsistencies from the dataset Identifying missing values Remove duplication Why should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions whe