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Learning Objectives - This module will give you an insight about what 'Machine Learning' is and How Apache Mahout algorithms are used in building intelligent applications.
Topics - Machine Learning Fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout and Hadoop, Introduction to Clustering, Classification.
Learning Objectives - In this module you will learn how to set up Mahout on Apache Hadoop. You will also get an understanding of Myrrix Machine Learning Platform.
Topics - Mahout on Apache Hadoop setup, Mahout and Myrrix.
Learning Objectives - In this module you will get an understanding of the recommendation system in Mahout and different filtering methods.
Topics - Recommendations using Mahout, Introduction to Recommendation systems, Content Based (Collaborative filtering, User based, Nearest N Users, Threshold, Item based), Mahout Optimizations.
Learning Objectives - In this module you will learn about the Recommendation platforms and implement a Recommender using MapReduce.
Topics - User based recommendation, User Neighbourhood, Item based Recommendation, Implementing a Recommender using MapReduce, Platforms: Similarity Measures, Manhattan Distance, Euclidean Distance, Cosine Similarity, Pearson's Correlation Similarity, Loglikihood Similarity, Tanimoto, Evaluating Recommendation Engines (Online and Offline), Recommendors in Production.
Learning Objectives - This module will help you in understanding 'Clustering' in Mahout and also give an overview of common Clustering Algorithms.
Topics - Clustering, Common Clustering Algorithms, K-means, Canopy Clustering, Fuzzy K-means and Mean Shift etc., Representing Data, Feature Selection, Vectorization, Representing Vectors, Clustering documents through example, TF-IDF, Implementing clustering in Hadoop, Classification.
Learning Objectives - In this module you will get a clear understanding of Classifier and the common Classifier Algorithms.
Topics - Examples, Basics, Predictor variables and Target variables, Common Algorithms, SGD, SVM, Navie Bayes, Random Forests, Training and evaluating a Classifier, Developing a Classifier.
Learning Objectives - At the end of this module, you will get an understanding of how Mahout can be used on Amazon EMR Hadoop distribution.
Topics - Mahout on Amazon EMR, Mahout Vs R, Introduction to tools like Weka, Octave, Matlab, SAS.
Learning Objectives - In this module you will develop an intelligent application using Mahout on Hadoop.
Topics - A complete recommendation engine built on application logs and transactions.
This course covers the fundamentals of machine learning techniques ranging from various algorithms of Support Vector Machines, k-means clustering, Random Forests, Collaborative filtering to recommendation system, Mahout on Hadoop and Amazon EMR, etc.
After the completion of Apache Mahout Course at Edureka, you should be able to:
1. Gain an insight into the Machine Learning techniques.
2. Understand the algorithms of SVM, Naive Bayes, Random Forests,etc.
3. Implement these using 'Apache Mahout'
4. Understand the recommendation system
5. Learn Collaborative filtering, Clustering and Categorization
6. Analyse Big Data using Hadoop and Mahout
7. Implementing a recommender using MapReduce
8. Introduction to tools like Weka, Octave, Matlab, SAS
This course is designed for all those who are interested in learning machine learning techniques in big data domain and write intelligent applications using Apache Mahout. The following professionals can go for this course :
1. Analytics Professionals
2. Data Scientists looking to hone their machine learning skills
3. Software Developers and Architects
4. Business Analysts wanting to learn Mahout for ML implementation
5. Professionals working with R, Matlab, Python, etc.
6. Statisticians looking to learn machine learning techniques
7. Graduates aspiring to take a leap in analytics domain
The basic Java and Hadoop knowledge is recommended and not mandatory as these concepts will also be covered during the course.
In the modern information age of exponential data growth, the success of companies and enterprises depends on how quickly and efficiently they turn vast amounts of data into actionable information. Whether it's for processing hundreds or thousands of personal e-mail messages a day or driving user intent from petabytes of weblogs, the need for tools that can organize and enhance data has never been greater. Therein lies the premise and the promise of the field of machine learning and Apache Mahout.
The concept was overwhelming. Will re-watch the video again. I like the pace of the class and the importance on minute details. This helps to create a mindset about what we are jumping into. This is very helpful specially when you are coming from non-java/non-unix background.
I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka!". Instructors are knowledgeable and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry.
I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop and after that since I'm still hungry I will take the Python class and so on because for me Edureka is the place to learn, people are really kind, every question receives the right answer. Thank you Edureka to make me a Data Scientist.
Definitely there is no doubt in saying that all the instructors at Edureka are industry experienced and the support staff provides a quick response to the tickets you log whether it be Day or Night. I like the way the sessions have been organized, with the Pre-requisites required for the next session and the assignments, QUIZ post session etc...I like the LMS a lot, You can find enough of required information in the forums. They even share the video recordings of other instructors as well in the LMS. So that if one couldn't get the content clearly in your session, you can always refer to other instructors recordings shared in your LMS. This part helped me in understanding few concepts in a better way.
The unique combination of Online Classes with 24*7 On-Demand Support and class recordings/ppt/docs etc in Learning Management System (LMS) on the site made me fall in love with edureka! There could not be a better Android Training Avenue to quench my thirst for learning Android Development.
I have been using Edureka for learning different topics related to Big Data -Hadoop, PIG, HIVE, Cassandra. I am very happy with the training and the help they are providing and I feel better than another online training where I registered for Cassandra. One of great thing is we can download the videos and references for later use, I use these in my commute to work (usually spend 2.5 hrs in train). Thank you for being flexible and proving great opportunity to learn cutting edge technologies - Cheers
I took PMP online classes with edureka. Just wanted to let you know that I was successfully able to pass the PMP exam couple of weeks ago. I enjoyed learning the concepts of PMP through edureka by the excellent laid out structure.Instructor's method of teaching was very helpful. During the classes he went over the concepts in detail and also clarified all the questions very patiently. He also shared his real world experiences which helped the student to relate to the topic. Even after the completion of course work, while reviewing the chapters in LMS, I struggled in few areas and when I reached out to the instructor without any hesitation, he explained it to me by providing some good examples.Wanted to take a moment to thank the people who contributed the most in my success.
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