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Macine Learning

Macine Learning

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Macine Learning

Analytics profession is to grow to $51b by 2016 making Machine Learning and R the most in-demand skills of our times. This course on Machine Learning with R introduces you to Machine Learning Algorithms with R, to help business organizations take informed decisions.

Introduction to Machine Learning
Understand the learning system model, training, testing, performance, Machine learning structure and the various learning techniques.
Nearest Neighbor Classification
Know about the instance based classifiers, nearest-neighbor classifiers. Master the difference between lazy and eager learning, understand k-NN variations, learn how to determine the good value for k and when to consider nearest neighbors. Learn condensing nearest neighbor issues and nearest Bayes classification.
Naive Bayes Learning
Learn conditional probability, master the basics of the Bayesian theorem, Bayes classifier, model parameters, naive Bayes training, types of errors, sensitivity and specificity, ROC curve, holdout estimation and cross-validation.
Decision Trees
Understand key requirements, decision tree as a rule set, how to create a decision tree and choosing attributes, ID3 heuristic, entropy, tree induction, splitting based on ordinal attributes. Determine the best split and the strength and weakness of decision trees.
Logistic Regression
Learn binary response regression model, linear regression output of proposed model and work on the problems with linear probability model. Understand logistic function, logistic regression, its interpretation, odds ratio, goodness of fit measures, confusion matrix.
Introduction to Cluster Analysis
Gain insight of types of data in cluster analysis, categorization of major clustering methods, partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based clustering methods and supervised classification.
Principal Component Analysis (PCA) and Forecasting Principles
Realize the curse of dimensionality, dimension reduction. Understand the importance of factor and component analysis, principal component analysis, basic time series and its components. Learn about the moving averages (simple & exponential), R’Â’s inbuilt function ts(), plotting of time series, business forecasting using moving average methods, the ARIMA model and the various application of ARIMA model in business.
interactive training 24 Hrs
Instructor Interaction Yes
Course material from DevOps Institute  Yes
Live Support Post Training 1 Year
Simulated Project 1
Real Time Project 2
Innomatics Tech Hub – Machine Learning Certificate  Yes
Exam voucher valid for an year.  Yes

Introduction to machine learning

  • What is machine learning?
  • Learning system model
  • Training and testing
  • Performance
  • Algorithms
  • Machine learning structure
  • What are we seeking?
  • Learning techniques

Module 2: Nearest neighbor classification

  • Instance based classifiers
  • Nearest-Neighbor classifiers
  • Lazy vs. Eager learning
  • k-NN variations
  • How to determine the good value for k
  • When to consider nearest neighbors
  • Condensing
  • Nearest neighbour issues

Module 3: Naive Bayes classification

  • Naive Bayes learning
  • Conditional probability
  • Bayesian theorem: basics
  • The Bayes classifier
  • Model parameters
  • Naive Bayes training
  • Types of errors
  • Sensitivity and specificity
  • ROC curve
  • Holdout estimation
  • Cross-validation

Decision Trees - Part I

  • Key requirements
  • Decision tree as a rule set
  • How to create a decision tree
  • Choosing attributes
  • ID3 heuristic
  • Entropy
  • Pruning trees – Pre and post
  • Subtree Replacement
  • Raising

Decision Trees - Part II

  • Tree induction
  • Splitting based on ordinal attributes
  • How to determine the best split
  • Measure of impurity: GINI
  • Splitting based on GINI
  • Attributes binay
  • Categorical -GINI
  • Strengths and weakness of decision trees

Ensemble Approaches

  • Ensemble approaches
  • Bagging model
  • Boosting
  • The AdaBoost algorithm
  • Gradient boosting
  • Random forests
  • RIF
  • RIC
  • Advantages
  • Disadvantages

Artificial Neural Network

  • Background of brain and neuron
  • Neural networks
  • Neurons diagram
  • Neuron models- step function
  • Ramp func etc
  • Perceptrons
  • Network architectures
  • Single-layer feed-forward

Artificial Neural Network continued

  • Multi layer feed-forward NN (FFNN)
  • Back propagation
  • NN design issues
  • Recurrent network architecture
  • Supervised learning NN
  • Self organizing map
  • Network structure
  • SOM algorithm

Why DevOps?

According to the Puppet Lab’s report, the organizations with DevOps foundation are experiencing high deployment codes with low failures.
This prospect is largely motivating the organizations to adopt DevOps, which is indirectly creating a huge requirement for the DevOps professionals in the current market.

What are the prerequisites of this course?

There are no specific prerequisites for this course; however, it is recommended to have:
1)Basic familiarity with IT industry
2)Basic Understanding of Unix/Linux environment
3)Knowledge of basic system operations and software installations

Who is the right candidate for this course?

This course is extensively useful for:
1)Software Testers, Architects, Build & Release Engineers, Automation Engineers, Integration Specialists and a lot more.
2)Organizations and aspirants, who are seeking a strong foundation on DevOps.

What are the software or hardware requirements?

The participants are recommended to have:
1)i3 or any higher range Processor with virtualization support
2)64-bit operating system
3)Minimum of 4 GB RAM (8 GB is recommended)
4)100 GB of free hard disk space.

What are the training materials provided?

For all the training modules that are covered in this course, adequate materials and good references will be provided. In the case of online interactive trainings, every session will be recorded and uploaded in the LMS, giving you the feasibility to recap the completed training sessions.

Course Reviews


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