In the final session of the series, we will explore the dimensionality reduction techniques. They are a part of unsupervised learning algorithms.
In this session, we will first explore what is curse of dimensionality and how does it impact a machine learning model and the overall solution. We will examine the techniques to reduce the number of dimenisons. Principal Component Analysis and Singular Value Decomposition will be studied in greater depth, their concept, mathematics behind them and pros and cons will be discussed in the session. Python code for both the techniques will be studied too.
Key points of discussion
- What is curse of dimensionality?
- What are dimensionality reduction techniques?
- Principal Component Analysis
- Singular Value Decomposition
- Implementation using Python
Who should attend
software engineers,software developers,data analysts,data scientists,Data Science,machine learnin,ai,artificial intelligence
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