Data Science - Data Cleaning & Feature Engineering for ML

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.31 GB | Duration: 6h 9m
Become expert in Data Cleaning and Feature Engineering for Machine Learning Data Science and Data Analytics

What you'll learn
Preprocessing the data takes 60%-70% of time. The course provides the entire toolbox to you to convert your raw data to model ready data
Become Expert in Python Pandas and scikit-learn for data manipulation and feature engineering
Become efficient in pre-processing data using various python packages such as pandas_profiling, catagory-encoders etc.
Learn feature Engineering techniques like encoding, imputation scaling etc. using Scikit-learn
Learn Scikit-learn Pipeline, Column tranformers to make the code readable and efficient
Learn to Write Python Functions which wraps various pandas functionalities to automate tasks
Export Analysis Output to Text file or Excel (export multiple dataframes to different sheets and multiple dataframes to same sheet in a
Requirements
Beginner level understanding of python is preferred but not mandatory
You'll need to install Anaconda and run jupyter notebook
Description
Real-life data are dirty. This is the reason why preprocessing tasks take approximately 70% of the time in the ML modeling process. Moreover, there is a lack of dedicated courses which deals with this challenging task
Introducing, "Data Science Course: Data Cleaning & Feature Engineering" a hardcore completely dedicated course to the most tedious tasks of Machine Learning modeling - "Data preprocessing".
if you want to enhance your data preprocessing skills to get better high-performing ML models, then this course is for you!
This course has been designed by experienced Data Scientists will help you to understand the WHYs and HOWs of preprocessing.
I will walk you step-by-step into the process of data preprocessing. With every tutorial, you will develop new skills and improve your understanding of preprocessing challenging ways to overcome this challenge
It is structured the following way
Part 1- EDA (exploratory Data Analysis): Get insights into your dataset
Part 2 - Data Cleaning: Clean your data based on insights
Part 3 - Data Manipulation: Generating features, subsetting, working with dates, etc.
Part 4 - Feature Engineering- Get the data ready for modeling
Part 5 - Function writing with Pandas Darframe
Who this course is for
Anyone who is interested to become efficient in data preprocessing
People who are learning data scientists and want to better understand the various nuances of data and its treatment
Budding data scientists who want to improve data preprocessing skills
Anyone who is interested in preprocessing part of data science
This course is not for people who want to learn machine learning algorithms
Who this course is for
Beginner ML enthusiast and ML engineers who want to improve their preprocessing and feature engineering skills
People who are programmers but want to enhance skill and get familiar with packages like Pandas and Scikit Learn

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