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Machine Learning with R -iPartner
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Machine Learning With R

  1. Machine Learning With R
8/12 weeks / 672*
(* including all taxes.)


Key Features
SMALL
BATCHES
MENTORING
BY EXPERTS
FLEXIBLE
SCHEDULE
LEARN
BY DOING
GOAL
ORIENTED



Course Agenda


  • What is machine learning?
  • Learning system model
  • Training and testing
  • Performance
  • Algorithms
  • Machine learning structure
  • What are we seeking?
  • Learning techniques
  • 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
  • Naïve Bayes Learning
  • Conditional Probability
  • Bayesian Theorem: Basics
  • The Bayes Classifier
  • Model Parameters
  • Naïve Bayes Training
  • Types of errors
  • Sensitivity and Specificity
  • ROC Curve
  • Holdout estimation
  • Cross-validation
  • 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
  • Ensemble Approaches
  • Bagging Model
  • Boosting
  • The AdaBoost Algorithm
  • Gradient Boosting
  • Random Forests
  • RIF, RIC
  • Advantages, Disadvantages
  • General Structure of Hunt’s Algorithm
  • 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

  • Background of Brain and Neuron
  • Neural Networks
  • Neurons Diagram
  • Neuron Models- step function ,ramp func etc
  • Perceptions
  • Network Architectures
  • single-layer feed-forward
  • Multi layer feed-forward NN (FFNN)
  • Backpropagation
  • NN DESIGN ISSUES
  • Recurrent Network Architecture
  • Supervised Learning NN
  • Self Organizing Map – Network Structure
  • SOM Algorithm
  • Support Vector Machines for Classification
  • Linear Discrimination
  • Nonlinear Discrimination
  • SVM Mathematically
  • Extensions
  • Application in Drug Design
  • Data Classification
  • Kernel Functions
  • Introduction to Regression
  • Why do Regression Analysis
  • Types of Regression Analysis
  • OLS Regression
  • Dependent And Independent Variable(s)
  • Steps To Implement A Regression Model
  • Simple Linear Regression
  • Understanding terminology of each of the output of linear regression
  • Correlation
  • Strength of Linear Association
  • Least-squares or regression line
  • Linear Regression Model
  • Correlation Coefficient, R
  • Multiple Regression
  • Regression Diagnostics
  • The assumptions
  • Assumption 1 and explanation
  • Residuals and non normality
  • Assumption 2 and explanation,Heteroscedasticity
  • Assumption 3 and explanation, Additivity
  • Assumption 4 and explanation, Linearity
  • Independence Assumption,Residual plots
  • Assumptions 5 to 8 and their explanations

  • Fitting the model
  • Diagnostic plots
  • Comparing models
  • Cross validation
  • Variable selection
  • Relative importance
  • AIC
  • Dummy variable
  • Box cox transformations
  • Understanding terminology of each of the output of linear regression
  • Residuels vs Fitted
  • Residuels vs Regression
  • Diagnostic Plots
  • Binary Response Regression Model
  • Linear Regression Output of proposed model
  • Problems with Linear Probability Model
  • Logistic Function
  • Logistic Regression & its interpretation
  • Odds Ratio
  • Goodness of fit measures
  • Confusion matrix
  • What is Cluster Analysis?
  • Types of Data in Cluster Analysis
  • A Categorization of Major Clustering Methods
  • Partitioning Methods
  • Hierarchical Methods
  • Density-Based Methods
  • Grid-Based Methods
  • Model-Based Clustering Methods
  • Supervised Classification
  • Curse of Dimensionality
  • Dimension Reduction
  • Why Factor or Component Analysis?
  • Principal Component Analysis
  • PCs, Variance and Least-Squares
  • Eigenvectors of a Correlation Matrix
  • Factor Analysis
  • PCA process Steps
  • Basic Time Series and it’s components
  • Moving Averages (Simple and Exponential)
  • R’s inbuilt function ts()
  • Plotting of time series
  • Business Forecasting using moving average methods
  • The ARIMA model
  • Application of ARIMA model in Business

Learn & Get

  • Gain in-depth understanding of JavaScript, MVC framework and Angular JavaScript Library
  • Understand what is AngularJs, its importance and real-world applications
  • Learn to create Controllers and sharing data between Controllers
  • Understand the concept of Dependency Injection
  • Learn the usage of angular-ui, ng-grid, angular-translate
  • Know about Node.js, Yo generator and Rest exposure
  • Create and load template using Routes
  • Write and Execute Business Logic and Validations in Controller
  • Understand the concept of AngularJS Integration with MVC
  • Learn to implement in real-time AngularJS examples
  • Understand AngularJS modules and their importance

Payment Method

PAYMENT METHODS
You need to pay through PayPal. We accept both Debit and Credit Card for transaction.
SCHOLARSHIPS
We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email info@ipartner.ca
FREQUENTLY ASKED QUESTIONS
In our iPartner self-paced training program, you will receive the training assessments, recorded sessions, course materials, Quizzes, related softwares and assignments. The courses are designed in such a way that you will the get real world exposure; the solid understanding of every concept that allows you to get the most from the online training experience and you will be able to apply the information and skills in the workplace. After the successful completion of your training program, you can take quizzes which enable you to check your level of knowledge and also enables you to clear your relevant certification at higher marks/grade where you will be able to work on the technologies independently.
In Self-paced courses, the learners are able to conduct hands-on exercises and produce learning deliverables entirely on their own at any convenient time without a facilitator whereas in the Online training courses, a facilitator will be available for answering queries at a specific time to be dedicated for learning. During your self-paced learning, you can learn more effectively when you interact with the content that is presented and a great way to facilitate this is through review questions and quizzes that strengthen key concepts. In case if you face any unexpected challenges while learning, we will arrange a live class with our trainer.
All Courses from iPartner are highly interactive to provide good exposure to learners and gives them a real time experience. You can learn only at a time where there are no distractions, which leads to effective learning. The costs of self-paced training are 75% cheaper than the online training. You will offer lifetime access hence you can refer it anytime during your project work or job.
Yes, at the top of the page of course details you can see sample videos.
As soon as you enroll to the course, your LMS (The Learning Management System) Access will be Functional. You will immediately get access to our course content in the form of a complete set of previous class recordings, PPTs, PDFs, assignments and access to our 24*7 support team. You can start learning right away.
24/7 access to video tutorials and Email Support along with online interactive session support with trainer for issue resolving.
Yes, You can pay difference amount between Online training and Self-paced course and you can be enrolled in next online training batch.
Please send an email. You can join our Live chat for instant solution.
We will provide you the links of the software to download which are open source and for proprietary tools, we will provide you the trail version if available.
You will have to work on a training project towards the end of the course. This will help you understand how the different components of courses are related to each other.
Classes are conducted via LIVE Video Streaming, where you get a chance to meet the instructor by speaking, chatting and sharing your screen. You will always have the access to videos and PPT. This would give you a clear insight about how the classes are conducted, quality of instructors and the level of Interaction in the class.
Yes, we do keep launching multiple offers that best suits your needs. Please email us at: info@ipartner.ca and we will get back to you with exciting offers.
We will help you with the issue and doubts regarding the course. You can attempt the quiz again.
Sure! Your feedbacks are greatly appreciated. Please connect with us on the email support - info@ipartner.ca.