DataHour: Exploring Dimensionality Reduction
DataHour: Exploring Dimensionality Reduction
26 Apr 202315:04pm - 26 Apr 202316:04pm
DataHour: Exploring Dimensionality Reduction
About the Event
In this DataHour session, we will be discussing the topic of Dimensionality Reduction, an important technique in the field of machine learning and data science. In this session, we will cover the definition and the importance of Dimensionality Reduction. We will also discuss the two popular types of Dimensionality Reduction techniques, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE).
The speaker will provide a detailed explanation of these techniques, along with their calculation and use cases. Moreover, we will also discuss the advantages and disadvantages of PCA and t-SNE. Finally, we will briefly touch upon other Dimensionality Reduction Techniques such as LLE, MDS, and Isomap. So, let's get started and explore the world of Dimensionality Reduction.
Prerequisites: The zeal for learning new technologies, and good to have a basic knowledge of Data Science.
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
Who is this DataHour for?
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
About the Speaker
Participate in discussion
Registration Details
Registered
Become a Speaker
Share your vision, inspire change, and leave a mark on the industry. We're calling for innovators and thought leaders to speak at our event
- Professional Exposure
- Networking Opportunities
- Thought Leadership
- Knowledge Exchange
- Leading-Edge Insights
- Community Contribution
