Course curriculum
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1
Preparing the Dataset for Machine Learning Model
- Resources to be used in this course
- Introduction to Problem Statement
- Reading Material - Understanding the Data
- ML-workflow
- Tasks to be Performed
- Combining Product Attribute Data with POS Data
- Combining all the tables in the Dataframe
- Understanding the Combined Data
- Treating Missing Values - Part 1
- Treating Missing Values Part - 2
- Outlier Detection and Treatment
- Preparing the Dataset for Supervised and Unsupervised Models
- Generative AI for Data Analysis
Data Processing on a Real World Problem Statement
This course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. By the end of the course, you will have mastered techniques like EDA and Missing Value Treatment.
Who Should Enroll:
Professionals: Individuals looking to expand their skill set on data cleaning and preparation.
Aspiring Students: For those setting out on their journey to become a data scientist and making a mark in the tech world.
Key Takeaways from the course
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Learn data preprocessing tasks
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Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios