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Learning Path: R: Complete Machine Learning & Deep Learning

Unleash the true potential of R to unlock the hidden layers of data
Instructor:
Packt Publishing
1,054 students enrolled
English [Auto-generated]
Develop R packages and extend the functionality of your model
Perform pre-model building steps
Understand the working behind core machine learning algorithms
Build recommendation engines using multiple algorithms
Incorporate R and Hadoop to solve machine learning problems on Big Data
Understand advanced strategies that help speed up your R code
Learn the basics of deep learning and artificial neural networks
Learn the intermediate and advanced concepts of artificial and recurrent neural networks

Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios.

The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering.

By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects.

Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

About the Authors

Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies.

Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing Big Data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building Big Data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process Big Data and apply data mining techniques for data analysis.

Vincenzo Lomonaco is a deep learning PhD student at the University of Bologna and founder of ContinuousAI, an open source project aiming to connect people and reorganize resources in the context of continuous learning and AI. He is also the PhD students’ representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses machine learning and computer architectures in the same department.

Mastering R Programming

1
The Course Overview

This video gives an overview of the entire course.

2
Performing Univariate Analysis

In this video, we will take a look at how to perform univariate analysis.

3
Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA

The goal of this video is to perform bivariate analysis in R using three cases.

4
Detecting and Treating Outlier

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

5
Treating Missing Values with `mice`

The goal of this video is to see how to treat missing values in R.

6
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.

7
Interpreting Regression Results and Interactions Terms

We'll see how to interpret regression results and Interaction effects in this video

8
Performing Residual Analysis & Extracting Extreme Observations Cook's Distance

In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance

9
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.

10
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.

11
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.

12
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.

13
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.

14
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.

15
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.

16
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.

17
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.

18
Building Classifiers with Support Vector Machines

In this video, we will look at how support vector machines work.

19
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.

20
Implementing Stochastic Gradient Boosting with GBM

Let's understand what boosting is and how stochastic gradient boosting works with GBM.

21
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.

22
Building Classifiers and Regressors with XGBoost

Let's look at how XG Boost works and how to implement it in this video.

23
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.

24
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.

25
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.

26
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.

27
Clustering with Affinity Propagation

How to use affinity propagation to cluster data points? How is it different from conventional algorithms?

28
Building Recommendation Engines

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

29
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.

30
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.

31
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.

32
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.

33
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.

34
Forecasting with ARIMA Modelling

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

35
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.

36
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.

37
Cosine Similarity and Latent Semantic Analysis

Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.

38
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.

39
Sentiment Scoring with tidytext and Syuzhet

Let's check out how to perform sentiment analysis and scoring in R.

40
Classifying Texts with RTextTools

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

41
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.

42
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.

43
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.

44
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.

45
ggplot2 Extensions and ggplotly

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

46
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.

47
Implementing Parallel Computing with doParallel and foreach

Let's tackle the implementation of parallel computing in R.

48
Writing Readable and Fast R Code with Pipes and DPlyR

The goal of this video is understand how to work with DplyR and pipes.

49
Writing Super Fast R Code with Minimal Keystrokes Using Data.Table

In this video, we will discuss how to manipulate data with the data.table package, how to achieve maximum speed, and what the various features of data.table are.

50
Interface C++ in R with RCpp

Our main focus in this video is to understand how to write C++ code and make it work in R. Also leverage the speed of C++ in R, interface Rcpp with R, and write Rcpp code.

51
Understanding the Structure of an R Package

We'll take a look at the components of an R package in this video.

52
Build, Document, and Host an R Package on GitHub

In this video, we will look at how to create an R Package so that it can be submitted to CRAN.

53
Performing Important Checks Before Submitting to CRAN

We will understand the mandatory checks and common problems faced by developers when creating R packages in this video.

54
Submitting an R Package to CRAN

The goal of this video is to show how to submit an R package to CRAN.

R Machine Learning solutions

1
The Course Overview

This is give you brief information about the course.

2
Downloading and Installing R

R must be first installed on your system to work on it.

3
Downloading and Installing RStudio

RStudio makes the process of development with R easier.

4
Installing and Loading Packages

R packages are an essential part of R as they are required in all our programs. Let's learn to do that.

5
Reading and Writing Data

You must know how to give data to R to work with data. You will learn that here.

6
Using R to Manipulate Data

Data manipulation is time consuming and hence needs to be done with the help of built-in R functions.

7
Applying Basic Statistics

R is widely used for statistical applications. Hence it is necessary to learn about the built in functions of R.

8
Visualizing Data

To communicate information effectively and make data easier to comprehend we need graphical representation. You will learn to plot figures in this section.

9
Getting a Dataset for Machine Learning

Because of some limitations, it is a good practice to get data from external repositories. You will be able to do just that after this video.

10
Reading a Titanic Dataset from a CSV File

Reading a dataset is the first and foremost step in data exploration. We need to learn to how to do that.

11
Converting Types on Character Variables

In R, since nominal, ordinal, interval, and ratio variable are treated differently in statistical modeling, we have to convert a nominal variable from a character into a factor.

12
Detecting Missing Values

Missing values affect the inference of a dataset. Thus it is important to detect them.

13
Imputing Missing Values

After detecting missing values, we need to impute them as their absence may affect the conclusion.

14
Exploring and Visualizing Data

After imputing the missing values, you should perform an exploratory analysis to summarize the data characteristics.

15
Predicting Passenger Survival with a Decision Tree

The exploratory analysis helps users gain insights into how single or multiple variables may affect the survival rate. However, it does not determine what combinations may generate a prediction model. We need to use a decision tree for that.

16
Validating the Power of Prediction with a Confusion Matrix

After constructing the prediction model, it is important to validate how the model performs while predicting the labels.

17
Assessing performance with the ROC curve

Another way of measuring performance is the ROC curve.

18
Understanding Data Sampling in R

When there are huge datasets, we can find the characteristics of the entire dataset with a part or sample of the data. Hence data sampling is essential.

19
Operating a Probability Distribution in R

Probability distribution and statistics are interdependent. To provide a justification to the statistical information, we need probability.

20
Working with Univariate Descriptive Statistics in R

Univariate statistics deals with a single variable and hence is very simple.

21
Performing Correlations and Multivariate Analysis

To analyze the relation among more than two variables, multivariate analysis is done.

22
Operating Linear Regression and Multivariate Analysis

Assessing the relation between dependent and independent variables is carried out through linear regression.

23
Conducting an Exact Binomial Test

To validate that the experiment results are significant, hypothesis testing is done.

24
Performing Student's t-test

To compare means of two different groups, one- and two-sample t-tests are conducted.

25
Performing the Kolmogorov-Smirnov Test

Comparing a sample with a reference probability or comparing cumulative distributions of two data sets calls for a Kolmogorov- Smirnov test.

26
Understanding the Wilcoxon Rank Sum and Signed Rank Test

The Wilcoxon Test is a non-parametric test for null hypothesis.

27
Working with Pearson's Chi-Squared Test

To check the distribution of categorical variables of two groups, Pearson's chi-squared test is used.

28
Conducting a One-Way ANOVA

To examine the relation between categorical independent variables and continuous dependent variables, Anova is used. When there is a single variable, one-way ANOVA is used.

29
Performing a Two-Way ANOVA

When there are two categorical values to be compared, two-way ANOVA is used.

30
Fitting a Linear Regression Model with lm

Linear regression is the simplest model in regression and can be used when there is one predictor value.

31
Summarizing Linear Model Fits

To obtain summarized information of a fitted model, we need to learn how to summarize linear model fits.

32
Using Linear Regression to Predict Unknown Values

It would be really convenient for us if we could predict unknown values. You can do that using linear regression.

33
Generating a Diagnostic Plot of a Fitted Model

To check if the fitted model adequately represents the data, we perform diagnostics.

34
Fitting a Polynomial Regression Model with lm

In the case of a non-linear relationship between predictor and response variables, a polynomial regression model is formed. We need to fit the model. This video will enable you to do that.

35
Fitting a Robust Linear Regression Model with rlm

An outlier will cause diversion from the slope of the regression line. In order to avoid that, we need to fit a robust linear regression model.

36
Studying a case of linear regression on SLID data

We will perform linear regression on a real-life example, the SLID dataset.

37
Reducing Dimensions with SVD

GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

38
Applying the Poisson model for Generalized Linear Regression

GLM allows response variables with error distribution other than a normal distribution. We apply the Poisson model to see how that is done.

39
Applying the Binomial Model for Generalized Linear Regression

When a variable is binary, we apply the binomial model.

40
Fitting a Generalized Additive Model to Data

GAM has the ability to deal with non-linear relationships between dependent and independent variables. We learn to fit a regression using GAM.

41
Visualizing a Generalized Additive Model

Visualizing a GAM helps it to understand better.

42
Diagnosing a Generalized Additive Model

You can also diagnose a GAM model to analyze it.

43
Preparing the Training and Testing Datasets

Training and testing datasets are both essential for building a classification model.

44
Building a Classification Model with Recursive Partitioning Trees

A partitioning tree works on the basis of split condition starting from the base node to the terminal node.

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