Mahout

• Mahout comprises three key components- environment for creating algorithms which are scalable, Scale and Spark + H20 algorithms and Hadoop’s Map Reduce algorithm.
• Apache Mahout is perfect for those who want to hitch a ride with commercial friendly machine learning for building apps which are intelligent. So, what makes Mahout the perfect launch pad?
• Well, it has a new math ecosystem called Samsara which is for renewal that is universal. It is a basic reconceptualization of custom made scalable machine learning algorithm.
• Mahout Samsara offers a whole new mathematical world with never before seen algorithms.
• At its base is linear algebra and data structures with statistical operations for support.
• It can be customized in Scala with extensions which are particular to Mahout.
• Mahout runs distributed operation on Spark clusters providing more ease of use and customization of algorithms besides making task submission easier.

About Apache Mahout Training:

Intelligent apps that are user friendly and learn from data as well as user behavior are no longer the domain of academicians and the corporate sector with massive research budget. Apache Mahout is needed to build intelligent apps with ease and rapidity.

Machine learning methods like collaborative filtering, categorization and clustering ensures that commonalities are found among larger groups of persons or automatic tagging of online content.

Mahout was co-founded by Grant Ingersoll who demonstrated machine learning with Mahout to cluster documents, organize recommendation and create content.

What makes a difference in today’s knowledge and information age is how quickly data translates into information that is actionable. Organizing and enhancing data is the premise of the field of machine learning.

What is Machine Learning?

This is a field in AI concerning techniques through which computers enhance outputs based on prior experiences. Field is closely linked to data mining and is used for everything from statistics to pattern recognition and probability theory.

Big companies like Yahoo and Amazon have used machine learning algorithm in apps. Learning from user behavior and past experience enables companies to really leverage from machine learning.

Machine learning is applicable in a wide range of fields from entertainment to fraud detecting and analyzing the stock market. When you see systems like Netflix which recommend products or items to users based on past experiences, you know you are experiencing the benefits of machine learning.

From categorizing web pages automatically to marking emails as trash, machine learning covers an entire gamut of myriad functions making it an invaluable tool for advancing technology.

While there are many approaches to machine learning, the two common ones which are given support by Mahout include the following:

• Supervised learning
• Unsupervised Learning

Supervised Learning: Making Sense of Data Using Examples

Supervised learning involves understanding a function on the basis of labelled training data for making predictions regarding valid input value. Supervised learning includes email categorization as spam, trash or junk, categorizing web pages according to genre or even speech or handwriting recognition. Algorithms are used for creating supervised learners and involve the use of natural networks and Naive Bayes classifier.

Unsupervised Learning: Understanding Data Without Instances

Unsupervised learning is focused on making sense of data without understanding what the correct or incorrect examples are. Trend recognition is a key example of this type of machine learning which incorporates self organizing maps and hierarchical clusters.

Mahout Training and Machine Learning:

Mahout carries out 3 basic approaches to machine learning namely:

1. Collaborative filters
2. Categories
3. Clusters

• Collaborative filters:

Collaborative filters are used by retail giants like Amazon which employ user information such as ratings and previous buys to make new recommendations to the site visitors. Books, music, DVDs, movies and other products where you need to limit the data to make a decision are the most common fields where collaborative filtering is used on online sites.  So, how does collaborative filtering work? It can be user or item based, slope one or model based.

User based collaborative filtering involves using similar users to recommend products- this is not easy to scale given the dynamic type of users

Item based collaborative learning involves calculation of commonalities between items and creation of recommendations on this basis. Offline computation is possible for this type of collaborative filtering.

Simple item based recommendations use slope one approaches where users are provided ratings, while model based item recommendations are associated with developing a perspective based on users and their ratings.

At the very base of collaborative filtering is the use of similarity between users and items which are rated. Different measures can be plugged in so you can check out what is best for you.

• Clusters:

When the data sets are large regardless of whether they are text or alpha numeric, similar or common items can be placed together. Grouping common items together requires the use of clustering whereby items are seen as vectors within a space of n dimensions.

Distance between 2 items on a vector can be measured using measures like Euclid’s distance, cosine similarity or Manhattan distance.

Actual clusters can be calculated through item grouping those which are near each other in distance. Many approaches exist for calculating the clusters. Mahout follows several different approaches for organizing items into clusters.

• Categories:

Last, but not the least is the use of categorization or classifying where unseen documents are labeled and placed together within a group. Classification approaches in machine learning use stats to create models to classify documents.

Therefore Machine learning is a very expansive and comprehensive concept and just how Apache Mahout helps out is given below.

About Apache Mahout:

Apache Mahout refers to an open source software project created by Apache Software foundation’s organization with the aim of coming up with machine learning algorithms which are scalable and at the same time free to use.

Mahout comes in various avatars including clustering, categorization, evolutionary programs and collaborative filters. Apache Hadoop library is also there to translate Mahout into cloud more efficiently.

So why the name Mahout? Well, a mahout is a Hindi world for a person who rides and takes care of the elephant. Apache Hadoop has a yellow elephant as its logo for symbolizing scalable operations and ease of tolerance of faults.

The use of the project’s Hadoop library has resulted in it earning the moniker Mahout.

Blast from the Past: All About Mahout’s History

Mahout project  commenced after people in an open search community called Apache Lucene expressed a desire for scalable and strong machine learning algorithms for classification, clustering and collaborative filtering to name a few.  Ng et al’s seminal paper “Map Reduce for Machine Learning on MultiCore” was the starting point for Mahout’s journey to a machine learning approach that is flawless.

Apache Mahout Training Features:

Apache Mahout is known for building and supporting users and contributors in a way such that the code survives any funding or inventor/ contributor to offer sustenance to the larger community.

Rather than cutting edge research with methods that are still unproven, Mahout is from the real world and relies on practical and efficient data use through excellent documentation and detailed instances.

Through this Mahout Training you will learn that, Mahout may have attained just a few decades in open source world, but it has a great deal of operational and functional significance especially with respect to the 3Cs- collaboration, clustering and collaborative filtering.

Mahout is one of the many projects atop Hadoop though Map Reduce is not always needed. Persons writing InterWebs can harness the power of mathematical tools and machine learning algorithm which uses Hadoop to reduce large data sets into manageable information.

Machine learning is a practical aspect of AI and is concerned with statistical techniques and probabilistic learning. Mahout also generates higher level abstraction based recommendations for popular social network or online commerce sites.

Mahout has an added advantage of offering user based recommendations and is far more than an e-commerce API. From finding clusters to classifying information, Mahout covers a wide field.

The aim behind Apache Mahout is to create adaptable machine learning because people in the data age are all for turning mounds of information into meaningful knowledge.

• Taking the First Steps with Mahout

Apache Mahout currently provides tools for creating recommendation engine via Taste library which supports user as well as item based recommendations. Taste has 5 key concepts namely user, items and preferences; data model,  user similarity, item similarity, recommender and user neighborhood.

Through the implementation of these components, complex recommendations are possible for offline or online purposes both. Taste also leverages Hadoop for making offline recommendations.

The architecture of Mahout is quite simple. There is a recommender which is the fundamental abstraction and provides recommendations based on data model. Data Model refers to interface about user preference data.

Databases are the most likely sources for this. Mahout also provides support for the boolean data model where users express association in an all or none manner rather than preference of differing types for items.

User Similarity defines commonality between users which forms a critical part of recommendation engines. Similarly, Item Similarity involves association between items.  Within the user based recommendations, recommenders are located by finding users similar to a given one.

User Neighborhood defines the locality within which similar users can be located. The conventional style recommender systems are the user based recommender.

Through this Mahout Training you will learn that, depending upon data, app, environments as well as performance requirements, Mahout is perfect for constructing the ideal recommender for the application. Trial and error works best though methods are also in place for the same.

The first step involves creating the model and providing a definition of user similarity. Next in line is the generation of recommendations. Outputs from the user recommendation are the next step.  Examples of similarity across items need to be located next. Similar items are then clustered, classified or filtered together.

• Mahout for Clustering

Mahout provides support for numerous clustering algorithm, composed in Map Reduce with their independent aims and criterion.

Some of the popular clustering techniques used in Mahout include canopy, k-means, mean shift and dirichlet. Canopy refers to an algorithm that is used to create the basis for numerous other clustering algorithm.

K Means and fuzzy K Means distinguish items into K clusters based on distance from centroid/ centre of earlier iteration.

Mean shift does not require prior knowledge about numerous clusters- clustering can be done on an arbitrary basis. When clusters are created using probabilistic models, a dirichlet results.

Here’s a step by step breakup of how clustering is done using Mahout. Firstly, the input is prepared. Text needs to convert to numerical firm. Hadoop driver programs which utilize Mahout can be used for running the algorithm for clustering followed by evaluation of results and iteration if required.

An important point is to note that such algorithm require information in a format which can be processed. Within machine learning, this is called vector/feature vectors. Mahout has two types of vector reps namely DenseVector and Sparse-Vector. Based on data, the right vector has to be chosen with the former being suitable for text based problems and the latter for non zero vectors.

Indexing the content into Lucene and creating vectors from its index is how you go about it. Clustering can be evaluated manually or in depth evaluation methods. To see if obtained results made sense, a manual approach is ideal.

A more rigorous approach is required as complexity of results are attained.  Once a set of vectors are created, k Means clustering algorithm is used along with the Mahout Job JAR situated in the directory of Hadoop.

You need to see the Hadoop resources for venturing into cloud computing using Mahout.

• Categorization using Mahout

Mahout supports dual approaches to categorization and classification of content namely the Naive Bayes classifier which is simply Map Reduce enabled or the complementary Naive Bayes approach. Each of these are discussed further.

The first approach is a simple Map Reduce enabled classifier approach whereby classifiers known to be precise and easy are used to break down data. Such classifiers are used when examples per classes are not in balance or data lacks independence.

The second approach corrects the problems of the first one while not doing away with rapidity and simplicity. Naive Bayes classifier involves two parts namely training and classification.

Once training as well as test sets are established, Train Classifier classes are run yielding large amount of implementation of Mahout atop Hadoop. Output from test in Mahout is placed in a confusion matrix which located how many results were classified in the correct and incorrect manner for each of the categories.

• Mahout-Moving Past the Obvious

Apache Mahout has moved fast with considerable capacity for further growth despite massive advances in collaborative filtering, categorization and clustering.

Later room for innovation includes Map Reduce implementations of decision forest for classification and association rules on random basis, topic identification in documents and other classification options using HBase.

Just as a mahout taps the skill and strength of the elephant, Apache Mahout too rides on the capabilities of Hadoop.

• Machine Learning Algorithms and Mahout:

Both sequence based as well as parallel machine learning algorithms are implemented through Apache Mahout. User as well as item based collaborative filtering is part of these algorithms.

Also associated with Mahout are Matrix Factorizations with ALS as well as that along with Implicity Feedback. There is weighted matrix factorization as well as logistic regression.

Key to classification is through Naive Bayes or Complementary Naive Bayes. Other algorithms include random forest, closed Markov models, multiple layer perceptron and spectral clustering are also other.

Don’t miss out on K means, fuzzy k means and streaming k means clustering. Other algorithms which Mahout also rides high on include singular value decomposition, PCA, QR Decomposition, Stochastic SVD, Latent Dirichlet Allocations, Concat Matrices, Row Similarity Job, Collocation and spare TF-IDF vector from texts.

Who Should Learn Apache Mahout Training?

• If you are looking to ride the wave, Apache Mahout is just the right tool. This machine learning library can empower everyone from developers to engineers.
• Enhanced calculations are possible through classification and collaborative filtering.
• Machine learning tasks are greatly enhanced using Apache Mahout. If you are looking to apply Apache Mahout, the fields where you can be aided range from statistics and probability to trend prediction.
• For example, consider the science of geology or meteorology. Mahout can be used to fill in data that is lacking from weather instrument or locate a particular mountain range in a given area.
• For instance, recommendation engine of Apache Mahout is ideal for building intelligent apps in a rapid and efficient manner.

Mahout Training Conclusion

• Apache Mahout offers the perfect ride for those looking to expand data organization and utilization.
• From uncovering patterns and trends behind large data sets to isolating clusters or classifying them into categories.
• Apache Mahout is an invaluable tool for the current information age.