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Unsupervised Learning | Clustering and Association Algorithms in Machine Learning | @edurekaIN
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This Edureka video on 'Unsupervised Learning' goes over the basics of Machine Learning, What is Unsupervised Learning in detail, its importance, types and algorithms, various applications and some of the major advantages and disadvantages that Unsupervised Learning poses. Below are the Topics Covered in this Machine Learning Tutorial Video:
0:46 - Agenda
1:26 - Overview of Machine Learning
3:27 - What is Unsupervised Learning?
5:50 - Importance of Unsupervised Learning
6:47 - Types of Unsupervised Learning
20:47 - Supervised vs. Unsupervised Learning
21:39 - Disadvantages of Unsupervised Learning
Subscribe to our channel to get video updates. Hit the subscribe button above.
#EdurekaUnsupervisedLearning #WhatIsUnsupervisedLearning #MachineLearning
How does it work?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you with a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in Python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modelling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behaviour from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
0:46 - Agenda
1:26 - Overview of Machine Learning
3:27 - What is Unsupervised Learning?
5:50 - Importance of Unsupervised Learning
6:47 - Types of Unsupervised Learning
20:47 - Supervised vs. Unsupervised Learning
21:39 - Disadvantages of Unsupervised Learning
Subscribe to our channel to get video updates. Hit the subscribe button above.
#EdurekaUnsupervisedLearning #WhatIsUnsupervisedLearning #MachineLearning
How does it work?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you with a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in Python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modelling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
- - - - - - - - - - - - - - - - - - -
Why learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behaviour from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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