Facebook Friend Recommendation using Graph Mining
Category: AI & Machine LearningFacebook Friend Recommendation using Graph Mining
Category: AI & Machine LearningStatement:
Facebook thrives on its users networking with new users, so the social media site offers inducements for users to add each other as friends. Sometimes, you may receive recommendations for other users you can add as a friend. Facebook does this so that you can reconnect with old friends and grow your network. You will be given a directed social graph, represented in a 2-column csv (source_node, destination_node), we need to predict the destination nodes for a give a source node.Data type: CSV files
Train data: train.csv (source node, destination node)
Data Size: 142MB
The AppliedAIProject attempts to teach students/course-participants some of the core ideas in machine learning, data-science and AI that would help the participants go from a real world business problem to a first cut, working and deployable AI solution to the problem. Our primary focus is to help participants build real world AI solutions using the skills they learn in this course. This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on this case study of real world AI problem and dataset to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to used in the real world.Key Points:
- Validity of this course is 240 days( i.e Starts from the date of your registration to this course)
- Expert Guidance, we will try to answer your queries in atmost 24hours
- 10+ machine learning algorithms will be taught in this course.
- No prerequisites-- we will teach every thing from basics ( we just expect you to know basic programming)
- Python for Data science is part of the course curriculum.
Target Audience:
We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:-
- Undergrad (BS/BTech/BE) students in engineering and science.
- Grad(MS/MTech/ME/MCA) students in engineering and science.
- Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.
Course Features
Lectures
Duration
100+ hours
Skill level
All levels
Language
English
Assessments
QUALIFICATION: Masters from IISC Bangalore, PROFESSIONAL EXPERIENCE: 11+ years of Experience( Yahoo Labs, Matherix Labs Co-founder, and Amazon)
Facebook Friend Recommendation using Graph Mining
Category: AI & Machine Learning
0 Comment(s)
Loading...
Pay easy monthly installments instead of lump-sum amount.
