Free Download Exploratory Data Analysis (EDA) for Machine Learning Free Download Udemy Course for free with Google Drive Download Link.
Master The Analysis and Transformation techniques done before the ML Project | Ensure Maximum Value for your data.
What you’ll learn
- How to identify outliers
- How to identify the relationship between variables
- Chi-Square Test & other tests
- Understanding of Python and basics of machine learning
Setting the context
Before you start a machine learning project, its important to ensure that the data is ready for modeling work. Exploratory Data Analysis (EDA) ensures the readiness of the data for Machine Learning. In fact, EDA ensures that the data is more usable. Without a proper EDA, Machine Learning work suffer from accuracy issues and many times, the algorithms won’t work.
Why is exploratory data analysis important in data science?
The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.
Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals.
Programming Language Used
Python: an interpreted, object-oriented programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components together.
Who this course is for:
- Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects
- Beginners in Machine Learning
Exploratory Data Analysis (EDA) for Machine Learning Download Link:
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