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.
Jul 25 | Sat,Sun (4 Weeks) Weekend Batch | Filling Fast 03:00 PM 05:00 PM |
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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.
Course Objectives:
After the completion of Apache Mahout Course at StepLeaf, 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
Who should go for this training?
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
Pre-requisites
The basic Java and Hadoop knowledge is recommended and not mandatory as these concepts will also be covered during the course.
hadoop, machinelearning, svm, canopyclustering, Artificial Intelligence, Mahout, Apache Mahout, Clustering, Myrrix, Recommendation Engine, Mahout Optimizations, recommender, recommendation platform, Fuzzy K-means, Mean Shift, Vectorization, TF-IDF, SGD, Random Forests, Amazon EMR, Mahout Vs R, Weka, Octave, Matlab, SAS
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.
Structure your learning and get a certificate to prove it.
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.
All the instructors at StepLeaf are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by StepLeaf for providing an awesome learning experience to the participants.