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- Types of variables
- Using Variables
- Logical Variables and Operators
- The "While" Loop
- Using the console
- The "For" Loop
- The "If" statement
- What is a Vector?
- Let's create some vectors
- Using the [] brackets
- Vectorized operations
- The power of vectorized operations
- Functions in R
- Packages in R
- Matrices
- Building Your First Matrix
- Naming Dimensions
- Colnames() and Rownames()
- Matrix Operations
- Visualizing With Matplot()
- Subsetting
- Visualizing Subsets
- Creating Your First Function
- Importing data into R
- Exploring your dataset
- Using the $ sign
- Basic operations with a Data Frame
- Filtering a Data Frame
- Introduction to qplot
- Building Dataframes
- Merging Data Frames
- Loop Functions
- lapply()
- sapply()
- apply()
- tapply()
- mapply()
- Regular Expressions
- Saving r objects
- dplyr package
- Shiny Apps

**R with Real Time Examples**

** Statistics with R**

- Data collection
- Data collection - Questionaire Designing
- Data collection - Observation
- Data collection - Case Study Method
- Qualitative Data Vs Quantitative Data
- Data Patterns
- Deciles Statistics
- Venn Diagram
- Central limit theorem
- Chebyshev's Theorem
- Kurtosis
- Normal Distribution
- Laplace Distribution
- Log Gamma Distribution
- Rayleigh Distribution
- Exponential distribution
- Multinomial Distribution
- Binomial Distribution
- Beta Distribution
- F distribution
- Negative Binomial Distribution
- Gamma Distribution
- Chi-squared Distribution
- Geometric Mean
- Harmonic Mean
- Outlier Function
- Stem and Leaf Plot
- Poisson Distribution
- Cumulative Poisson Distribution
- Inverse Gamma Distribution
- Continuous Uniform Distribution
- Hypergeometric Distribution
- Harmonic Number
- Gumbel Distribution
- Comparing plots
- Power Calculator
- Process Sigma
- Harmonic Resonance Frequency
- Standard normal table
- Pooled Variance (r)
- Mean Deviation
- Means Difference
- code(+theory) for Descriptive:
- entral tendency:
- Arithmetic Mean
- Arithmetic Median
- Arithmetic Mode
- Arithmetic Range
- Range Rule of Thumb
- Adjusted R-Squared
- Standard Deviation
- Relative Standard Deviation
- Analysis of Variance
- Grand Mean
- Boxplots
- Quartile Deviation
- Frequency Distribution
- Bar Graph
- Dot Plot
- Scatterplots
- Correlation Co-efficient
- Pie Chart
- Histograms
- Cumulative Frequency
- Cumulative plots
- Skewness
- Goodness of Fit
- Transformations
- Trimmed Mean
- Reliability Coefficient
- Linear regression
- Logistic Regression
- Quadratic Regression
- Regression Intercept Confidence Interval
- Residual sum of squares
- Equation Sum of Square
- Standard Error ( SE )
- Root Mean Square
- Cohen's kappa coefficient
- Ti 83 Exponential Regression
- Shannon Wiener Diversity Index

**Machine Learning with Real Time Examples**

- Building Linear Regressors
- Interpreting Regression Results and Interactions Terms
- Performing Residual Analysis & Extracting Extreme Observations Cook's Distance
- Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
- Validating Model Performance on New Data with k-Fold Cross Validation
- Building Non-Linear Regressors with Splines and GAMs
- Building Logistic Regressors, Evaluation Metrics, and ROC Curve
- Understanding the Concept and Building Naive Bayes Classifier
- Building k-Nearest Neighbors Classifier
- Building Tree Based Models Using RPart, cTree, and C5.0
- Building Predictive Models with the caret Package
- Selecting Important Features with RFE, varImp, and Boruta
- Building Classifiers with Support Vector Machines
- Understanding Bagging and Building Random Forest Classifier
- Implementing Stochastic Gradient Boosting with GBM
- Regularization with Ridge, Lasso, and Elasticnet
- Building Classifiers and Regressors with XGBoost
- Dimensionality Reduction with Principal Component Analysis
- Clustering with k-means and Principal Components
- Determining Optimum Number of Clusters
- Understanding and Implementing Hierarchical Clustering
- Clustering with Affinity Propagation
- Building Recommendation Engines
- Understanding the Components of a Time Series, and the xts Package
- Stationarity, De-Trend, and De-Seasonalize
- Understanding the Significance of Lags, ACF, PACF, and CCF
- Forecasting with Moving Average and Exponential Smoothing
- Forecasting with Double Exponential and Holt Winters
- Forecasting with ARIMA Modelling
- Scraping Web Pages and Processing Texts
- Corpus, TDM, TF-IDF, and Word Cloud
- Cosine Similarity and Latent Semantic Analysis
- Extracting Topics with Latent Dirichlet Allocation
- Sentiment Scoring with tidytext and Syuzhet
- Classifying Texts with RTextTools
- Building a Basic ggplot2 and Customizing the Aesthetics and Themes
- Manipulating Legend, AddingText, and Annotation
- Drawing Multiple Plots with Faceting and Changing Layouts
- Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
- ggplot2 Extensions and ggplotly
- Implementing Best Practices to Speed Up R Code Preview
- Implementing Parallel Computing with doParallel and foreach
- Writing Readable and Fast R Code with Pipes and DPlyR
- Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
- Interface C++ in R with RCpp
- Understanding the Structure of an R Package
- Build, Document, and Host an R Package on GitHub
- Performing Important Checks Before Submitting to CRAN
- Submitting an R Package to CRAN
- R Machine Learning solutions
- Downloading and Installing R
- Downloading and Installing RStudio
- Installing and Loading Packages
- Reading and Writing Data
- Using R to Manipulate Data
- Applying Basic Statistics
- Visualizing Data
- Getting a Dataset for Machine Learning
- Reading a Titanic Dataset from a CSV File Preview
- Converting Types on Character Variables
- Detecting Missing Values
- Imputing Missing Values
- Exploring and Visualizing Data
- Predicting Passenger Survival with a Decision Tree
- Validating the Power of Prediction with a Confusion Matrix
- Assessing performance with the ROC curve
- Understanding Data Sampling in R
- Operating a Probability Distribution in R
- Working with Univariate Descriptive Statistics in R
- Performing Correlations and Multivariate Analysis
- Operating Linear Regression and Multivariate Analysis
- Conducting an Exact Binomial Test
- Performing Student's t-test
- Performing the Kolmogorov-Smirnov Test
- Understanding the Wilcoxon Rank Sum and Signed Rank Test
- Working with Pearson's Chi-Squared Test
- Conducting a One-Way ANOVA
- Performing a Two-Way ANOVA
- Fitting a Linear Regression Model with lm
- Summarizing Linear Model Fits
- Using Linear Regression to Predict Unknown Values
- Generating a Diagnostic Plot of a Fitted Model
- Fitting a Polynomial Regression Model with lm
- Fitting a Robust Linear Regression Model with rlm
- Studying a case of linear regression on SLID data
- Reducing Dimensions with SVD
- Applying the Poisson model for Generalized Linear Regression
- Applying the Binomial Model for Generalized Linear Regression
- Fitting a Generalized Additive Model to Data
- Visualizing a Generalized Additive Model
- Diagnosing a Generalized Additive Model
- Preparing the Training and Testing Datasets
- Building a Classification Model with Recursive Partitioning Trees
- Visualizing a Recursive Partitioning Tree
- Measuring the Prediction Performance of a Recursive Partitioning Tree
- Pruning a Recursive Partitioning Tree
- Building a Classification Model with a Conditional Inference Tree
- Visualizing a Conditional Inference Tree
- Measuring the Prediction Performance of a Conditional Inference Tree
- Classifying Data with the K-Nearest Neighbor Classifier
- Classifying Data with Logistic Regression
- Classifying data with the Naïve Bayes Classifier
- Classifying Data with a Support Vector Machine
- Choosing the Cost of an SVM
- Visualizing an SVM Fit
- Predicting Labels Based on a Model Trained by an SVM
- Tuning an SVM
- Training a Neural Network with neuralnet
- Visualizing a Neural Network Trained by neuralnet
- Predicting Labels based on a Model Trained by neuralnet
- Training a Neural Network with nnet
- Predicting labels based on a model trained by nnet
- Estimating Model Performance with k-fold Cross Validation
- Performing Cross Validation with the e1071 Package
- Performing Cross Validation with the caret Package
- Ranking the Variable Importance with the caret Package
- Ranking the Variable Importance with the rminer Package
- Finding Highly Correlated Features with the caret Package
- Selecting Features Using the Caret Package
- Measuring the Performance of the Regression Model
- Measuring Prediction Performance with a Confusion Matrix
- Measuring Prediction Performance Using ROCR
- Comparing an ROC Curve Using the Caret Package
- Measuring Performance Differences between Models with the caret Package
- Classifying Data with the Bagging Method
- Performing Cross Validation with the Bagging Method
- Classifying Data with the Boosting Method
- Performing Cross Validation with the Boosting Method
- Classifying Data with Gradient Boosting
- Calculating the Margins of a Classifier
- Calculating the Error Evolution of the Ensemble Method
- Classifying Data with Random Forest
- Estimating the Prediction Errors of Different Classifiers
- Clustering Data with Hierarchical Clustering
- Cutting Trees into Clusters
- Clustering Data with the k-Means Method
- Drawing a Bivariate Cluster Plot
- Comparing Clustering Methods
- Extracting Silhouette Information from Clustering
- Obtaining the Optimum Number of Clusters for k-Means
- Clustering Data with the Density-Based Method
- Clustering Data with the Model-Based Method
- Visualizing a Dissimilarity Matrix
- Validating Clusters Externally
- Transforming Data into Transactions
- Displaying Transactions and Associations
- Mining Associations with the Apriori Rule
- Pruning Redundant Rules
- Visualizing Association Rules
- Mining Frequent Itemsets with Eclat
- Creating Transactions with Temporal Information
- Mining Frequent Sequential Patterns with cSPADE
- Performing Feature Selection with FSelector
- Performing Dimension Reduction with PCA
- Determining the Number of Principal Components Using the Scree Test
- Determining the Number of Principal Components Using the Kaiser Method
- Visualizing Multivariate Data Using biplot
- Performing Dimension Reduction with MDS
- Reducing Dimensions with SVD
- Compressing Images with SVD
- Performing Nonlinear Dimension Reduction with ISOMAP
- Performing Nonlinear Dimension Reduction with Local Linear Embedding
- Preparing the RHadoop Environment
- Installing rmr2
- Installing rhdfs
- Operating HDFS with rhdfs
- Implementing a Word Count Problem with RHadoop
- Comparing the Performance between an R MapReduce Program & a Standard R Program
- Testing and Debugging the rmr2 Program
- Installing plyrmr
- Manipulating Data with plyrmr
- Conducting Machine Learning with RHadoop
- Configuring RHadoop Clusters on Amazon EMR
- Deep Learning with R
- Introduction to Multi-hidden-layer Architectures
- Fundamental Concepts in Deep Learning
- Introduction to Artificial Neural Networks
- Classification with Two-Layers Artificial Neural Networks
- Probabilistic Predictions with Two-Layer ANNs
- Introduction to Multi-hidden-layer Architectures
- Tuning ANNs Hyper-Parameters and Best Practices
- Neural Network Architectures
- Neural Network Architectures Continued
- The LearningProcess
- Optimization Algorithms and Stochastic Gradient Descent
- Backpropagation
- Hyper-Parameters Optimization
- Introduction to Convolutional Neural Networks
- Introduction to Convolutional Neural Networks Continued
- CNNs in R
- Classifying Real-World Images with Pre-Trained Models
- Introduction to Recurrent Neural Networks
- Introduction to Long Short-Term Memory
- RNNs in R
- Use-Case – Learning How to Spell English Words from Scratch
- Introduction to Unsupervised and Reinforcement Learning
- Autoencoders
- Restricted Boltzmann Machines and Deep Belief Networks
- Reinforcement Learning with ANNs
- Use-Case – Anomaly Detection through Denoising Autoencoders
- Deep Learning for Computer Vision
- Deep Learning for Natural Language Processing
- Deep Learning for Audio Signal Processing
- Deep Learning for Complex Multimodal Tasks
- Other Important Applications of Deep Learning
- Debugging Deep Learning Systems
- GPU and MGPU Computing for Deep Learning
- A Complete Comparison of Every DL Packages in R
- Research Directions and Open Questions

**Big Data & Hadoop with Real Time Examples**

- Introduction to Big Data
- Getting started with Hadoop
- Hive Concepts
- Pig Concepts
- Use case on log collections & analysis
- Use Case on Ecommerece

**Take Away – Data Science Training in Jaipur, Data Science Certification Courses in Jaipur**