Machine Learning with R Certification Training

  4.4 Ratings
  3362 Learners

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.

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Course Overview
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.
Highly Experienced
Mentors
Develop 2 Production-Quality Angular2 Projects
Lifetime Access to Dashboard
24x7
Coding Support
Internationally Recognized Certification
Course Syllabus
  • 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
  • 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
  • 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
  • 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
  • Bagging model
  • Boosting
  • The AdaBoost algorithm
  • Gradient boosting
  • Random forests
  • RIF
  • RIC
  • Advantages
  • Disadvantages
  • Background of brain and neuron
  • Neural networks
  • Neurons diagram
  • Neuron models- step function
  • Ramp func etc
  • Perceptrons
  • Network architectures
  • Single-layer feed-forward
  • Multi layer feed-forward NN (FFNN)
  • Back propagation
  • NN design issues
  • Recurrent network architecture
  • Supervised learning NN
  • Self organizing map
  • Network structure
  • SOM algorithm
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • 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
  • Fitting the model
  • Diagnostic plots
  • Comparing models
  • Cross validation
  • Variable selection
  • Relative importance
  • AIC
  • Dummy variable
  • Box cox transformations
  • 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 & exponential)
  • R'Â’s inbuilt function ts()
  • Plotting of time series
  • Business forecasting using moving average methods
  • The ARIMA model
  • Application of ARIMA model in business
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
Projects Which Students Will Develop
Classification Model with Algorithms
This project aims at creating a classification models for mushroom data set with different classification algorithms.
Naave Bayes Classification Algorithm
This project aims at creating Naave Bayes classification algorithm to classify the people as republican and democrats.
Using MNIST Data
This project aims to classify handwritten digits using the famous MNIST data.
Bank Special Service
This project is based on a survey based on the special services offered by Banks in order to compete with their rivals.
Clustering Wine data
This project aims at clustering the wine data to determine the quantities of 13 constituents found in each of the three types of wines grown in Italy.
Customers Feedback
FAQ's
Machine learning is the science of getting computers to act without being explicitly programmed. In this course, you will learn about the most effective machine learning techniques and gain experience by implementing them and getting them to work for you. More importantly, you'll learn about not only the theoretical part but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI (Artificial Intelligence)
Graduates, post-graduates, and Ph.D scholars (from the statistical background), entry-level software professionals and anyone who is interested in data analytics career can do this course.
This course provides a broad introduction to machine learning, data mining and statistical and pattern recognition. The course will also enable you to implement numerous case studies and applications so that you'll also learn how to apply learning algorithms. It is the world’s most powerful programming language for statistical computing and graphics making it a must for Data Scientists.
  • Microsoft® Windows® 7/8/10 (32- or 64-bit)
    • 4 GB RAM(Recommended)
    • I3 or higher processor
After doing this course, one will have good knowledge of statistics such as:
  • Supervised learning (classification algorithms, support vector machines, neural networks).
  • Unsupervised learning (clustering, recommender systems).
  • Best practices in machine learning.
These skills are enough to start a career as Data Scientist.