Amazon Fashion Discovery Engine using Machine Learning
Amazon Fashion Discovery Engine
Srikanth Varma,
Lead DSML Instructor at Scaler
About this Free Amazon Fashion Discovery Engine using Machine Learning Course
Embark on a journey into the world of e-commerce recommendation systems with our Amazon Fashion Discovery Engine course. Learn to leverage product descriptions and images to recommend similar apparel products to users. From data cleaning and understanding to advanced text and visual similarity techniques, discover how to build a robust fashion discovery engine tailored to Amazon's e-commerce platform. Join us as we delve into the intricacies of content-based recommendation and unlock the potential of personalized shopping experiences.
5
Audio: English
Subtitles: English
Duration
4h 41m (1 Modules)Course Level
BeginnerCertificate
IncludedWhat you’ll learn
The skills that you would learn after taking up this Amazon Fashion Discovery Engine using Machine Learning online course are:
- How to use Amazon product advertising API to retrieve product information.
- Data folders and paths setup for storing and accessing the data.
- Overview of the data and terminology used in the e-commerce domain.
- Data cleaning techniques, including handling missing data and understanding duplicate rows.
- Text pre-processing techniques such as tokenization, stop-word removal, and stemming.
- Bag of words and TF-IDF methods for featurizing text data.
- Text semantics-based similarity using Word2Vec.
- Utilizing brand and color information to improve similarity calculation.
- Building a real-world solution for apparel recommendation.
Course Content
1 Modules | 28 Lessons | 4h 41m
Certificate for Free Amazon Fashion Discovery Engine using Machine Learning
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Instructor of this course
Lead DSML Instructor at Scaler
2000+ Students on Scaler Platform
600+ Hours of Lectures Delivered
5 Star Instructor on Scaler
9 Courses
- Co-Founder & Principal Instructor, Applied AI & AppliedRoots
- Senior ML Scientist @ Amazon, Palo Alto and Bangalore
- Co-Founder, Matherix Labs
- Research Engineer, Yahoo! Labs
- Masters from IISc Bangalore, Gate 2007(AIR 2)
- 13 years of experience in AI and Machine Learning
Key Features of this Amazon fashion discovery engine Course
- Gain insights into e-commerce recommendation systems and their application in the fashion industry.
- Learn to leverage product descriptions and images to recommend similar apparel items effectively.
- Master techniques for handling missing data and duplicates to ensure data quality.
- Explore methods such as TF-IDF and Word2Vec for building text-based product similarity models.
- Dive into ConvNets for extracting features from images and building visual similarity models.
- Understand how to build and deploy a fashion discovery engine tailored to Amazon's e-commerce platform.
- Measure the effectiveness of your solution through A/B testing to optimize user experience and engagement.
Pre-requisites for Amazon fashion discovery engine Course
Prior to embarking on this course, familiarity with the following concepts is recommended:
- Familiarity with programming concepts and preferably experience with Python.
- Basic knowledge of machine learning concepts, such as supervised and unsupervised learning.
- Proficiency in data analysis techniques using libraries like pandas and numpy.
- Basic understanding of image processing concepts and libraries like OpenCV.
- Familiarity with deep learning concepts, particularly convolutional neural networks (CNNs).
- Understanding of e-commerce concepts and familiarity with recommendation systems is beneficial.
Who should learn this Amazon fashion discovery engine Course?
This course is perfect for:
- Data scientists aiming to specialize in e-commerce and recommendation systems.
- E-commerce professionals seeking to enhance their understanding of recommendation engines.
- Developers interested in implementing recommendation systems in the fashion domain.
- Fashion enthusiasts looking to explore the intersection of technology and fashion.
- Anyone passionate about leveraging data to create personalized shopping experiences in the e-commerce industry.