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Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data. There are three main types of Machine Learning: supervisedlearning, unsupervised learning, and reinforcement learning.
For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.
What supervisedlearning methods did you use? The early days of the effort were spent on EDA and exchanging ideas with other members of the community. Do you have any advice for those just getting started in data science? Much of your time will be spent on datapreparation and feature engineering.
Our focus will be hands-on, with an emphasis on the practical application and understanding of essential machine learning concepts. Attendees will be introduced to a variety of machine learning algorithms, placing a spotlight on logistic regression, a potent supervisedlearning technique for solving binary classification problems.
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