Personal Information
Organização/Local de trabalho
Bengaluru Area, India, Karnataka India
Cargo
Data Science and Deep Learning Trainer
Setor
Technology / Software / Internet
Site
statinfer.com
Sobre
Conducted 5000+ hours training on Data Science and related tools
Author of the book “Practical Business Analytics using SAS”
Experience in credit risk model building, market response model building, social media analytics, revenue forecasting and machine learning
Specializations: Data Science, Advanced Analytics, Predictive Modeling, Machine Learning, Data Mining, Data Visualization, Text Mining, Bigdata, AzureML
Tools:R, Python, SAS and AzureML
Marcadores
data analysis & predictive modeling course
data analysis
machine learning
data scientist
predictive modeling
bigdata
deep learning
r
hyper parameters
neural networks
sas functions
sas programs
sas
business analytics
data mining
tableau
python
clasification
datasets
hadoop
k-means clustering
arima forecasting
trends & forecasting
analytics
data
artificial intelligence
bias variance tradeoff
variance
bias
under fitting
over fitting
auc
roc
f1 score
specificity
sensitivity
bootstrap cross validation
10-fold cross validation
k-fold cross validation
cross validation
model selection
tensor board
regularization
learning rate
statinfer
r code options
r code
boosting algorithm
boosting
gradient boosting
gbm
ann
code
ai
gradient descent
back propagation
information gain
decision tree
entropy
r packages
r functions
r data
model building
logistic regression
model validation
vintage analysis
variable selection
waterfall analysis
credit risk
risk analytics
step by step learning
p-value
t-test
case study
testing of hypothesis
analysis
time series
tableau options
presenting data
graphs
dash boards
data visualizations
sql
ruby
qlikview
database
data visualization
kernal
svm
learning
fa
pca
bigdata sources
data sources
need of bigdata
r basics
clutsre analysis
data validation
data sanitization
data exploration
stationarity
ar process
ma process
goodness of fit
big data
baby hadoop meetup
understanding data
benchmark analysis
overall summary & summary by various segments
data cleaning & audit
objective & scope
kpis
background
control charts
multivariate analysis & segmentation
tracking basic metrics
driver analysis
Ver mais
Apresentações
(31)Documentos
(4)Gostaram
(6)How to Become a Data Scientist
ryanorban
•
Há 9 anos
Apache Spark
Uwe Printz
•
Há 9 anos
Excel/R
Andrija Djurovic
•
Há 11 anos
Enabling R on Hadoop
DataWorks Summit
•
Há 10 anos
Big data analytics
Rahul Kulkarni
•
Há 10 anos
Syoncloud big data for retail banking, Syoncloud
Ladislav Urban
•
Há 10 anos
Personal Information
Organização/Local de trabalho
Bengaluru Area, India, Karnataka India
Cargo
Data Science and Deep Learning Trainer
Setor
Technology / Software / Internet
Site
statinfer.com
Sobre
Conducted 5000+ hours training on Data Science and related tools
Author of the book “Practical Business Analytics using SAS”
Experience in credit risk model building, market response model building, social media analytics, revenue forecasting and machine learning
Specializations: Data Science, Advanced Analytics, Predictive Modeling, Machine Learning, Data Mining, Data Visualization, Text Mining, Bigdata, AzureML
Tools:R, Python, SAS and AzureML
Marcadores
data analysis & predictive modeling course
data analysis
machine learning
data scientist
predictive modeling
bigdata
deep learning
r
hyper parameters
neural networks
sas functions
sas programs
sas
business analytics
data mining
tableau
python
clasification
datasets
hadoop
k-means clustering
arima forecasting
trends & forecasting
analytics
data
artificial intelligence
bias variance tradeoff
variance
bias
under fitting
over fitting
auc
roc
f1 score
specificity
sensitivity
bootstrap cross validation
10-fold cross validation
k-fold cross validation
cross validation
model selection
tensor board
regularization
learning rate
statinfer
r code options
r code
boosting algorithm
boosting
gradient boosting
gbm
ann
code
ai
gradient descent
back propagation
information gain
decision tree
entropy
r packages
r functions
r data
model building
logistic regression
model validation
vintage analysis
variable selection
waterfall analysis
credit risk
risk analytics
step by step learning
p-value
t-test
case study
testing of hypothesis
analysis
time series
tableau options
presenting data
graphs
dash boards
data visualizations
sql
ruby
qlikview
database
data visualization
kernal
svm
learning
fa
pca
bigdata sources
data sources
need of bigdata
r basics
clutsre analysis
data validation
data sanitization
data exploration
stationarity
ar process
ma process
goodness of fit
big data
baby hadoop meetup
understanding data
benchmark analysis
overall summary & summary by various segments
data cleaning & audit
objective & scope
kpis
background
control charts
multivariate analysis & segmentation
tracking basic metrics
driver analysis
Ver mais