Learning Path: R: Complete Machine Learning and Deep Learning Solutions

Mastering R Programming. 54 lectures 05:12:20 Mastering R Programming - The Course Overview This video gives an overview of the entire course. Performing Univariate Analysis In this video, we will take a look at how to perform univariate analysis.

• Learn the use of univariate analysis on continuous variables and what the metrics are
• Understand how to perform univariate analysis on categorical variables
• See how to compute the metrics in R

Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA The goal of this video is to perform bivariate analysis in R using three cases.

• Perform bivariate analysis using correlation analysis
• Perform bivariate analysis using ANOVA
• Perform bivariate analysis using the Chi-Sq statistic

Detecting and Treating Outlier In this video, we will see how to detect and treat outliers.

• Check out how to detect outliers in a continuous variable
• Get to know the ways to treat outliers
• See how to code in R

Treating Missing Values with `mice` The goal of this video is to see how to treat missing values in R.

• Understand the different types of missing values
• Look at the ways to treat missing values
• Check out how to code in R

Building Linear Regressors In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.

• The purpose of linear regression and the concept
• How to build regression model in R
• How to predict and compute accuracy measures.

Interpreting Regression Results and Interactions Terms We'll see how to interpret regression results and Interaction effects in this video

• Explain the summary of regression results
• Explain the various terms
• Add interaction term to the model

Performing Residual Analysis and Extracting Extreme Observations With Cook's Distance In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance

• Explain the meaning behind residual plots and its interpretation
• Explain Cook's Distance, its meaning and significance
• Implement them in R

Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.

• Explain Best subsets and do it in R
• Stepwise regression
• Compare models using ANOVA

Validating Model Performance on New Data with k-Fold Cross Validation In this video we will see how to do k-fold cross validation in R.

• Explain the concept
• Show how to implement it in R

Building Non-Linear Regressors with Splines and GAMs The goal of this video is check out how to build non-linear regression models using Splines and GAMs.

• Explain the concept behind splines
• Implement splines in R
• Explain and implement GAMS

Building Logistic Regressors, Evaluation Metrics, and ROC Curve Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.

• Explain the concept behind logistic regression
• Understand the evaluation metrics and interpretation of the ROC curve
• Implement in R

Understanding the Concept and Building Naive Bayes Classifier In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.

• Understand the concept and implementation of naïve Bayes by learning conditional probability and Bayes rule
• Solve a mathematical example
• Implement in R

Building k-Nearest Neighbors Classifier In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.

• Explain the concepts
• Show how it works
• How to implement it in R

Building Tree Based Models Using RPart, cTree, and C5.0 The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.

• Explain how decision trees work and their usage
• Show the concepts of cTree, rpart and C5.0
• Implement in R

Building Predictive Models with the caret Package The goal of this video is know what the various features of the caret package are and how to build predictive models.

• Explain the workflow
• Show preprocessing, model tuning, control parameters, and parallelization
• Implement in R

Selecting Important Features with RFE, varImp, and Boruta The goal of this video is to know how to do feature selection before building predictive models.

• Explain the concepts behind RFE, Boruta and variable importance
• Show the references for variable importance
• Implement in R

Building Classifiers with Support Vector Machines In this video, we will look at how support vector machines work.

• Explain kernel trick, hyper places, linearly non-separable case
• Show how to tune with various kernels
• Implement in R

Understanding Bagging and Building Random Forest Classifier In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.

• How does bagging work
• How does random forests work
• Understand the implementation in R

Implementing Stochastic Gradient Boosting with GBM Let's understand what boosting is and how stochastic gradient boosting works with GBM.

• Explain boosting and how the algorithm works
• How to tune it
• Show how to implement in R

Regularization with Ridge, Lasso, and Elasticnet In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.

• Learn the concept of regularization and shrinkage methods
• Understand Ridge and Lasso regression, and how are they different
• Implement in R using the glmnet package

Building Classifiers and Regressors with XGBoost Let's look at how XG Boost works and how to implement it in this video.

• Understand the driving principle behind XG Boost
• Understand how to the structure of tuning parameters
• Implement in R

Dimensionality Reduction with Principal Component Analysis Our goal in this video would be to reduce the dimensionality of data with principal components, and understand the concept and how to implement it in R.

• Understand the purpose and concepts behind principal component analysis
• Plot it and interpret the principal components and Biplot
• Implement in R

Clustering with k-means and Principal Components In this video, we will understand the k-means clustering algorithm and implement it using the principal components.

• Understand k-means
• Perform clustering with principal components
• Implement in R

Determining Optimum Number of Clusters In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.

• Explain the Hopkins statistic for clustering tendency
• Explain silhouette width for optimal number of clusters
• Implement in R

Understanding and Implementing Hierarchical Clustering The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.

• Understand the logic
• Understand the linkage methods
• Implement in R

Clustering with Affinity Propagation How to use affinity propagation to cluster data points? How is it different from conventional algorithms?

• Explain the general concept behind the working of affinity propagation algorithm
• Show how it is different from k-means and hierarchical clustering
• Implement in R, plot it, and solve a mini challenge

Building Recommendation Engines How to build recommendation engines to recommend products/movies to new and existing users?

• Explore the various approaches to make recommendations
• Explain the collaborative filtering algorithm
• Implement it R and solve coding challenges using POPULAR and association mining methods

Understanding the Components of a Time Series, and the xts Package The goal of this video is to understand what a time series is, how to create time series of various frequencies, and the enhanced facilities available in the xts package.

• Create a time series and understand its components
• Get an overview of the xts package
• Implement in R

Stationarity, De-Trend, and De-Seasonalize The goal of this video is to understand the characteristics of a time series: stationarity and how to de-trend and de-seasonalize a time series.

• How to make a time series?
• How to de-trend and de-seasonalize
• Implement in R

Understanding the Significance of Lags, ACF, PACF, and CCF In this video, we will introduce the characteristics of time series such as ACF, PACF, and CCF; why they matter; and how to interpret them.

• Explain lags, ACF, PACF, and CCF
• Interpret the meaning
• Implement in R

Forecasting with Moving Average and Exponential Smoothing Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.

• Explain moving average
• Explain exponential smoothing
• Implement in R

Forecasting with Double Exponential and Holt Winters In this video, we will understand how double exponential smoothing and holt winter forecasting works, when to use them, and how to implement them in R.

• How double exponential smoothing works and how it is different from holt winters
• Understand the calculations
• Implement in R

Forecasting with ARIMA Modelling Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.

• Understand the full form and components of the ARIMA model
• Explain how ARIMA works, its orders and configurations, and how to choose the p,d and q
• Implement auto.arima() in R

Scraping Web Pages and Processing Texts In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data.

• Show a web scraping example with rvest
• Explain the structure of a typical webpage and basics of HTML and extract selector paths
• Process and clean text data

Corpus, TDM, TF-IDF, and Word Cloud Our goal in this video is to know how to process texts using tm package and understand the significance of TF-IDF and its implementation. Finally, we see how to draw a word cloud in R.

• Explain the features of tm package
• Explain TF-IDF and itsuse
• Draw a word cloud from a text document

Cosine Similarity and Latent Semantic Analysis Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.

• Explain the concept behind cosine similarity
• Explain LSA
• Implementation in R with challenge

Extracting Topics with Latent Dirichlet Allocation In this video, we will see how to extract the underlying topics in a document, the keywords related to each topic and the proportion of topics in each document.

• Explain the concept behind LDA
• Show a real application to extract topics from wiki docs
• Show implementation and interpretation in R

Sentiment Scoring with tidytext and Syuzhet Let's check out how to perform sentiment analysis and scoring in R.

• Show facilities in the tidytext package, the workflow and lexicons
• Show a Syuzhet application
• Implement in R

Classifying Texts with RTextTools How to classify texts with machine learning algorithms using the RTextTools package?

• Explain the general workflow
• Explain facilities in RTextTools
• Implement in R

Building a Basic ggplot2 and Customizing the Aesthetics and Themes The goal of this videos is to understand what is the basic structure of to make charts with ggplot, how to customize the aesthetics, and manipulate the theme elements.

• Explain the structure of ggplot and how it is different from base graphics
• Modify the aesthetics and theme
• Implement in R and solve a challenge

Manipulating Legend, AddingText, and Annotation In this video, we will see how to manipulate the legend the way we want and how to add texts and annotation in ggplot.

• Show the syntax to manipulate the legend
• Add texts using geom_text and add annotations using grob
• Implement in R

Drawing Multiple Plots with Faceting and Changing Layouts The goal of this video is to understand how to plot multiple plots in the same chart and how to change the layouts of ggplot.

• Show how to use facet_wrap and facet_grid to make multiple charts for various levels of one or two variables
• Show how to change the layout of a ggplot using the gridExtra package
• Implement in R and solve a challenge on what was covered

Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.

• Explain the syntax for various plots
• Learn Scatterplot, Jitter, Counts plot, Bar chart, Histogram, Box plot, Violin plot, Time series chart, Multiple Time Series, and Ribbon Area
• Implement in R and practice with a mini challenge

ggplot2 Extensions and ggplotly In this video, we will understand what the popular ggplot extensions are, and where to find them, and their applications.

• Show where to find the ggplot extensions
• Show the usage of ggfortify, ggthemes, ggrepel and so on
• Show how to implement in R

Implementing Best Practices to Speed Up R Code We will discuss the best practices that should be followed to minimize code runtime in this video ion, and reducing condition checks inside for loops

Understand the smart usage of the which() function, and also the use of ifelse, apply family, and byte code compilation

Show how to implement in R with working examples

Implementing Parallel Computing with doParallel and foreach Let's tackle...