Spark MLlib Tutorial | Machine Learning On Spark | Apache Spark Tutorial | Simplilearn

This video on Spark MLlib Tutorial will help you learn about Spark's machine learning library. You will understand the different types of machine learning algorithms - supervised, unsupervised, and reinforcement learning. Then, you will get an idea about the various tools that Spark's MLlib component provides. You will see the different data types and some fundamental statistical analysis that you can perform using MLlib. Finally, you will understand about classification and regression algorithms and implement it using linear and logistic regression. Now, let's get started and learn Spark MLlib.

Below topics are explained in this Spark MLlib tutorial:
1. What is Spark MLlib? 00:42
2. What is Machine Learning? 02:27
3. Machine Learning Algorithms 04:51
4. Spark MLlib Tools 09:14
5. Spark MLlib Data Types 09:55
6. Machine Learning Pipelines 22:18
7. Clasification & Regression 24:13
8. Spark MLlib Use Case Demo 31:51

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[subramanianchenniappan]

Great thanks. I am a java developer who have hadoop handson and also had simplilearn account

[blackwidowalibaba]

hi, now i have an account to learn big data analysis according to simplilearn. This video is very clear to help me understand how to create label and feature of MLLIB. when i practice to vectorasssembler, there is handleInvalid=error, why? thanks

[amithnambiar]

What is the command prompt used ? Can we not use Python for this with regards to Spark ?

[isaacl]

Hello, may I have a copy of the dataset?

[explorertraveller]

can you please provide github link for dataset linear regression ??

[mahammadshoyab]

Hi Ajay, would you provide a github link which you have shown in this video. I am being tried with your ajaykuma/ScalaApps I have been getting error.Thanks and you have given good explanation . Waiting to here more algos from you.

[aparajita]

Where can I learn & get certify for this?

[hemadrinaidu]

worst explanation. you are just reading like news paper. What ever you are reading than hole content is available in spark official documentation. Reading is not useful but discussion is helpful to learners.