APACHE MAHOUT ONLINE COURSE

Mildaintrainings brings you an In-depth Boot Camp session on Apache Mahout the Machine Learning library that simplifies extracting information from huge data sets & is a popular choice for organizations that work with Big Data. Machine Learning with Apache Mahout Online course is conducted by industry expert trainers.Enroll & Get Certified now!

  • ✔ Course Duration : 30 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed
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30 hrs

Course Duration

20+

Countries And Counting

25+

Corporates Served

20+ hrs

Workshop

APACHE MAHOUT ONLINE COURSE

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human. Machine learning is so extensive today that you probably use it many times a day without knowing it.

Machine learning with python is one of the most important new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics & healthcare to retail. This has led to intense curiosity about Machine Learning field among many students & working professionals in the field. Machine learning with Python is a one of the field of computer science that gives computer systems the ability to “learn” progressively improves performance on a specific task with data. Machine Learning with Python course plunged into the basics of machine learning using an approachable, & well-known, programming language. You’ll learn about Supervised Vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

What you will Learn

Machine learning with python is one of the most important new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics & healthcare to retail. This has led to intense curiosity about Machine Learning field among many students & working professionals in the field. Machine learning with Python is a one of the field of computer science that gives computer systems the ability to “learn” progressively improves performance on a specific task with data. Machine Learning with Python course plunged into the basics of machine learning using an approachable, & well-known, programming language. You’ll learn about Supervised Vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

PREREQUISITES

  • Basic knowledge of mathematics is required especially linear algebra, calculus, probability, Matrices.
  • Some basic knowledge of coding
  • If you some knowledge of a language like R, Python it will have an extra edge.
  • At least high school level math skills will be required

CURRICULUM

Learning Objectives:
  • Machine Learning Fundamentals
  • Apache Mahout Basics
  • History of Mahout
  • Supervised and Unsupervised Learning techniques
  • Mahout and Hadoop
  • Introduction to Clustering and Classification
Learning Objectives:
  • Mahout on Apache Hadoop
  • Setup Mahout and Myrrix
Learning Objectives:
  • Recommendations using Apache Mahout
  • Introduction to Recommendation systems
  • Content Based Mahout Optimizations
Learning Objectives:
  • 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
  • Log likelihood
  • Similarity
  • Tanimoto Evaluating
  • Recommendation Engines
  • Recommendors
  • in Production
Learning Objectives:
  • Common Clustering Algorithms in Apache mahout
  • K-means Canopy Clustering
  • Fuzzy K-means and Mean Shift etc
  • Representing Data Feature Selection
  • Vectorization in Apache Mahout training
  • Representing Vectors
  • Clustering documents through example TF-IDF
  • Implementing clustering in Hadoop Classification
Learning Objectives:

Examples

Basic Predictor variables and Target variables

Common Algorithms

SGD

SVM

Navie bayes

Random Forest

Training and evaluating a Classifier

Developing a Classifie

 

Learning Objectives:
  • Mahout on Amazon
  • EMR Mahout Vs R
  • Introduction to tools like Weka
  • Octave
  • Matlab and SAS
Learning Objectives:
  • A complete recommendation engine built on application logs and transactions

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

Just give us a CALL at +91 8447121833 OR email at [email protected]

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