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Machine Learning

New Batch Starts on 28th July 2018

his is well defined Course content  by Top Level industrial experts and Senior  Professors .  Altogether 250 hours programme it takes around 3 months  with 15 + case studies  List of Case Studies .  with  R Programming, Python Programming

Chapter 1   : Introducing Machine Learning  

Machine learning successes

The limits of machine learning

Machine learning ethics

 How machines learn   

Data storage

Abstraction

Generalization

Evaluation

 Machine learning in practice     

Types of input data

Types of machine learning algorithms

Matching input data to algorithms

 Machine learning with R      

Installing R packages

Loading and unloading R packages

 Chapter2    : Managing and Understanding Data       

R data structures      

Vectors

Factors

Lists

Data frames

Matrixes and arrays

 Managing data with R      

Saving, loading, and removing R data structures

Importing and saving data from CSV files

 Exploring and understanding data      

Exploring the structure of data

Exploring numeric variables

Measuring the central tendency – mean and median

Measuring spread – quartiles and the five-number summary

Visualizing numeric variables – boxplots

Visualizing numeric variables – histograms

Understanding numeric data – uniform and normal distributions

Measuring spread – variance and standard deviation

Exploring categorical variables

Measuring the central tendency – the mode

Exploring relationships between variables

Visualizing relationships – scatterplots

Examining relationships – two-way cross-tabulations

Chapter 3    : Lazy Learning – Classification Using

Nearest Neighbors      

Understanding nearest neighbor classification      

The k-NN algorithm

Measuring similarity with distance

Choosing an appropriate k

Preparing data for use with k-NN

Why is the k-NN algorithm lazy?

Transformation – z-score standardization

Testing alternative values of k

Chapter 4   : Probabilistic Learning – Classification

Using Naive Bayes      

Understanding Naive Bayes    

Basic concepts of Bayesian methods

Understanding probability

Understanding joint probability

Computing conditional probability with Bayes’ theorem

The Naive Bayes algorithm

Classification with Naive Bayes

The Laplace estimator

Using numeric features with Naive Bayes

 Chapter 5   : Divide and Conquer – Classification Using

Decision Trees and Rules        

Understanding decision trees        

Divide and conquer

The C 45  .  decision tree algorithm

Choosing the best split

Pruning the decision tree

Understanding classification rules        

Separate and conquer

The   R algorithm

The RIPPER algorithm

Rules from decision trees

What makes trees and rules greedy?

 Chapter6    : Forecasting Numeric Data – Regression Methods       

Understanding regression        

Simple linear regression

Ordinary least squares estimation

Correlations

Multiple linear regression

Correlation

SLR Regression

MLR Regression

Examination Residual analysis

Auto Correlation

Test of ANOVA Significant

VIF Analysis

Test of Ttest Significant

CP Indexing

Eigen Value for PCA Analysis

Homoscedasticity

Heteroskedasticity

Stepwise regression

Forward Regression

Backward Regression

Multicollinearity

Cross validation

MAPE

Check prediction accuracy

Standized regression

Transformed Regression

Dummy Variables Regression

Chapter7  : classifion regression characters data 

  Logistic Regression

 Discriminate Regression Analysis

 Multiple Discriminant Analysis

 Stepwise Discriminant Analysis

 Logit function

 Test of Associations

 Chi-square strength of association

 Binary Regression Analysis

 Profit and Logit Models

 Estimation of probability using logistic regression,

 Wald Test statistics for Model

 Hosmer Lemshow

 Nagurkake R square

 Pseudio R square

 Maximum likelihood estimation

 Model Fit

 Model cross validation

 Discrimination functions

 AIC

 BIC (Bayesian information criterion)

 Kappa Statistics

 AIC

 BIC

 Error/ Confusion matrices

 ROC

 APE

 MAPE

 Lift Curve

 Sensitivity

 Misclassification Rating

 Specificity

 Maximum Absolute Error

 Recall

 Miss classification

 Root Final Prediction Error

 Gini Coefficient

 Schwarz’s Bayesian Criterion

 Chapter8  : Dimension Reduction Analysis

 Introduction to Factor Analysis

 Principle component analysis

 Reliability Test

 KMO MSA tests, Eigen Value Interpretation,

 Rotation and Extraction steps

 Varmix Models

 Conformity Factor Analysis

 Exploitary Factor Analysis

 Factor Score for Regression

Chapter9 : Cluster Analysis

 Introduction to Cluster Techniques

 Hierarchical clustering

 K Means clustering

 Wards Methods

 Agglomerative Clustering

 Variation Methods

 Maximum distance Linkage Methods

 Centroid distance Methods

 Minimum distance Linkage Method

 Cluster Dengogram,

 Ecludin distance method s

 Chapter 10    : Black Box Methods – Neural Networks and

Support Vector Machines        

Understanding neural networks       

From biological to artificial neurons

Activation functions

Network topology

The number of layers

The direction of information travel

The number of nodes in each layer

Training neural networks with backpropagation

Understanding Support Vector Machines         

Classification with hyperplanes

The case of linearly separable data

The case of nonlinearly separable data

Using kernels for non-linear spaces

Chapter11    : Finding Patterns – Market Basket Analysis Using

Association Rules         

Understanding association rules       

The Apriori algorithm for association rule learning

Measuring rule interest – support and confidence

Building a set of rules with the Apriori principle

 Chapter12    : Evaluating Model Performance       

Measuring performance for classification        

Working with classification prediction data in R

A closer look at confusion matrices

Using confusion matrices to measure performance

Beyond accuracy – other measures of performance

The kappa statistic

Sensitivity and specificity

Precision and recall

The F-measure

Visualizing performance trade-offs

ROC curves

Estimating future performance         

The holdout method

Cross-validation

Bootstrap sampling

GPU computing

Deploying optimized learning algorithms

Building bigger regression models with biglm

Growing bigger and faster random forests with bigrf

Training and evaluating models in parallel with caret

Course Curriculum

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