Machine Learning with Mahout Training:
Riding on Scalable Algorithms
- 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 learningfor 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.
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.
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.
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:
- Collaborative filters
- Categories
- Clusters
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.
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.
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.
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.
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.
For any query call/miss call-
Mob-+918587999769
Section 1: Introdution
1 What is Mahout
2 Mahout Architecture
3 Subversion Installation
4 Item Based Recommendation
5 Example- CBayes Classifier
6 Command Line Options
7 Canopy Clustering
Section 2: Understanding Recommender
8 Basic Recommender
9 Practical Examples
10 Mahout Seqdumper Command
11 Running Code through Eclipse
12 Reading from Code
Section 3: Apache Mahout Deep Dive
13 Introduction to Apache Mahout Deep Dive
14 Use Cases
15 Recommendation
16 Example – Tanimoto Distance
17 How to Use Mahout?
18 Exercise
19 Example – Evaluation
20 Deep Dive Canopy Clustering
Section 4: Classification
21 Classification
22 Vector File
23 Naïve Bayes Classifier from Code
24 KMeans Clustering
25 Logistic Regression
