What is Machine Learning? | Machine Learning Basics | Machine Learning Tutorial | Edureka

1. Evolution of Machine Learning
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. Unsupervised Learning
6. Reinforcement Learning

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#MachineLearning #PythonMachineLearning #PythonMachineTraining

How it Works?
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 a Grade and a Verifiable Certificate!

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About the Course

Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.

Customer Review

Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."

[jayaprakashm]

I'm speechless, the video i was looking for. Do you guys have video on Probability and stats for Data science?

[GodGurdjieff]

Very very thank you sir, I searched many channels, but edureca is a unique one, such a simple and better explanation, mai aapka ehsaan kabhi nhi bhoolunga, i learnt so much from you😘😘😘

[aspinc]

Great job sir... Thank you for your videos... And what are the algorithms used in reinforcement ML

[priyamkakati]

One of the best channels i refer to.Great work guys.

[morphin]

Wow thank you for this . It was very helpful .

[deepikachintala]

I just want to take time to appreciate the effort you guys are putting in delivering quality content to the viewers .. I always follow your videos and suggest them to friends as well, and they're very helpful .. Edureka has helped me in shaping my career ..
I hope you continue this effort going forward, and provide more good content ..

[nazifasadia]

Wow...Such a clear explanation.Thanks.

[virapan]

Thank you for an insightful video on machine learning. It's very informative especially for people like me who has no prior knowledge of ML.
I have a question.
Whether the number of clusters in unsupervised learning needs to be predefined ? or does it create new cluster if it finds any of the previous clusters are not suitable ? Is there any limit on number of clusters it can create ?

[chandanadas]

great explanation. it's really helpful for me thanku sir.

[saradamundluru]

Sir you have cleared my confusion differences between ai, ml, dl. And I have another thing to clear with in these three subjects which I would learn

[parikashyap]

your voice is amazing.... well nice tutorial gr8 explanation simple and qualitative one. Thanks

[azhergadoo]

It was an honour to be here will you please tell me What’s the trade-off between bias and variance? Thankyou so much

[sanakhan-lttv]

Very useful content
Great work keep uploading videos like this

[hemanthkumar]

Video was good I got clear information

[indumathiadhikesavalu]

Nice explanation with simplistic examples. Bravo👏

[jayantkeer]

Your videos content are awesome keep uploading videos like this.

[palakurthisrija]

More examples on reinforcement learning..

[sparshadevapalli]

one doubt in unsupervised learning, if there is no training data set or past data to train the model, the input data comes in and based on the behavioral characteristics we are clustering, then how come "Recommendation to customers based on past purchase" come under unsupervised learning. Kindly correct me if I am wrong.. What is the difference between this category (Shopping/Retail) example pertaining to supervised and Unsupervised learning? How does it differ?

[hv]

I think at 11.07 heading of slide should be "Unsupervised Learning Algorithms".

[poonamkumari]

you said, in retail domain, system recommend the products to customer based on past purchase so this should comes under supervised learning as we have labeled data or labeld products ...could you please clarify it please?