Machine Learning in Python
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Updated On 02 Feb, 19
Machine Learning Tutorial in Python helps you gain expertise in various types of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Through this playlist you will be learning the important Machine Learning concepts and its implementation in python programming language.
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** 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
Check out the Entire Machine Learning Playlist: https://bit.ly/2NG9tK4
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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 Masters Program Covers Topics LIke:
Machine Learning Techniques and Artificial Intelligence Types
Named Entity Recognition
Bayesian and Markov’s Models
Policy Gradient Methods.
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.
Please write back to us at firstname.lastname@example.org or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.