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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 ProICT, 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.