What you would learn in Data Science: Data Cleaning & Feature Engineering for ML course?
Real-life data are dirty, and the preprocessing task takes about 70 percent of this ML modeling process. Furthermore, there is a shortage of courses that deal with the difficult task.
We are introducing "Data Science Course: Data Cleaning & Feature Engineering," a complete and hard-core committed course that focuses on the most challenging task involved in Machine Learning modeling - "Data preprocessing."
If you are looking to improve your skills in data processing to create better models for ML, Then this course is perfect for you!
This course, developed by highly experienced Data Scientists, will help you learn the WHYs and HOWs of preprocessing.
I will guide you step-by-step through the process of preprocessing. Through each tutorial, you'll develop new abilities and increase your knowledge of the preprocessing process and methods to tackle this problem.
It is organized according to the following format:
Part 1 EDA (exploratory Data Analysis) Gain insights into your data
Part 2Data Cleaning: Cleanse your data using insights
Part 3 3. Data manipulation: Generating new features, subsetting and working with dates, and more.
Fourth Part -Feature Engineering- Prepare the data to be used in modeling
Part 5Function writing using Pandas Darframe
Who is this course intended for:
Anyone who would like to be efficient in the data processing
Students who are learning to become data scientists and would like to be aware of the many details of data and how it is used
Data scientists in the making are eager to develop their skills in data preprocessing.
Anyone interested in data science's preprocessing.
The program is not meant for people looking to master machine learning algorithms.
Content of the Course:
Processing the data requires between 60% and 70 percent of the time. The course gives you the complete toolbox that you need to transform your raw data into model-ready data.
Develop into an expert Learn to be an Expert Python Pandas and scikit-learn for feature engineering and manipulation of data
Be more efficient at pre-processing data with various python tools like pandas_profiling Catagory-encoders.
Learn about feature engineering techniques such as Imputation scaling, encoding, etc. by using Scikit-learn
Learn about Scikit-learn Pipeline. Column transformers for making the program more readable and efficient
Learn to write Python Functions that wrap various pandas functions to automatize tasks
Export Analysis Output for Export to a text file, and Excel (export several data frames to multiple sheets) and multiple data frames to the same worksheet in a workbook programmatically
Download Data Science: Data Cleaning & Feature Engineering for ML from below links NOW!
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