Principal Component Analysis in Python | Basics of Principle Component Analysis Explained | Edureka

** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka session on Principal Component Analysis (PCA) will help you understand the concepts behind dimensionality reduction and how PCA can be used to deal with high dimensional data.

Here’s a list of topics that will be covered in this session:

1. Need For Principal Component Analysis
2. What is PCA?
3. Step by step computation of PCA
4. Principal Component Analysis With Python

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About the Masters Program

Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.

The Master's Program Covers Topics LIke:

Python Programming
PySpark
HDFS
Spark SQL
Machine Learning Techniques and Artificial Intelligence Types
Tokenization
Named Entity Recognition
Lemmatization
Supervised Algorithms
Unsupervised Algorithms
Tensor Flow
Deep learning
Keras
Neural Networks
Bayesian and Markov’s Models
Inference
Decision Making
Bandit Algorithms
Bellman Equation
Policy Gradient Methods.

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Prerequisites

There are no prerequisites for enrolment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry.

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