Facebook Friend Recommendation using Graph Mining
Facebook Friend Recommendation using Graph Mining
About this Free Facebook Friend Recommendation using Graph Mining Course
Embark on a transformative journey into the realm of social network analysis with our this course. Delve into the fascinating world of graph theory and mining techniques as we explore the intricacies of understanding and leveraging connections within social networks. From uncovering hidden patterns to developing advanced recommendation systems, this course equips learners with the tools and expertise needed to navigate the complexities of modern-day social networking platforms. Join us as we unravel the secrets of Facebook friend recommendation and harness the power of graph mining to enhance user experiences and connectivity.
What you’ll learn
- The basics of graphs, including nodes/vertices, edges/links, directed edges, and paths.
- Different data formats used for representing graphs and their limitations.
- How to map graph-related problems to supervised classification tasks.
- Considerations for business constraints and metrics when working with graph data.
- Exploratory data analysis (EDA) techniques for analyzing graph data, including basic statistics and follower/following stats.
- How to frame binary classification tasks based on graph data.
- Methods for splitting graph data into training and testing sets.
- Techniques for feature engineering on graphs, such as Jaccard and Cosine similarities.
Course Content
Certificate for Free Facebook Friend Recommendation using Graph Mining
Instructor of this course
- 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 Facebook Friend Recommendation using Graph Mining Course
Embark on a transformative journey into the realm of facebook friend recommendation using graph mining equipped with the knowledge and skills to make a meaningful impact in the graph theory. By enrolling, you will:
- Understand the problem definition and intricacies of Facebook friend recommendation.
- Explore the fundamentals of graphs, including nodes, edges, and paths.
- Gain insights into data format and limitations inherent in social network data.
- Learn to map Facebook friend recommendation to a supervised classification problem.
- Identify business constraints and key metrics for evaluating recommendation systems.
- Conduct exploratory data analysis (EDA) to uncover basic statistics and follower/following trends.
Pre-requisites for Facebook Friend Recommendation using Graph Mining Course
Prior to embarking on this course, familiarity with the following concepts is recommended:
- Understanding of fundamental concepts in graph theory.
- Proficiency in Python programming for implementing algorithms and data manipulation.
- Familiarity with supervised classification techniques.
- Basic knowledge of machine learning and its applications.
- Prior experience with data exploration and visualization.
- Comfortable working with data manipulation libraries such as pandas and NumPy.
- Knowledge of social network analysis concepts is advantageous but not essential.
Who should learn this Facebook Friend Recommendation using Graph Mining Course for Beginners?
This course is perfect for beginners who are:
- Enthusiastic about exploring the fascinating realm of graph mining and social network analysis.
- Interested in understanding how algorithms drive recommendations on social media platforms like Facebook.
- Eager to dive into the world of machine learning and its applications in real-world scenarios.
- Seeking to enhance their Python programming skills while delving into practical data analysis techniques.
- Curious about leveraging data to uncover hidden patterns and insights within social networks.
- Aspiring data scientists or analysts looking to expand their knowledge and skill set in graph-based recommendation systems.