Learning Path: R: Complete Machine Learning & Deep Learning

Learning Path: R: Complete Machine Learning & Deep Learning, available at $44.99, has an average rating of 4.45, with 213 lectures, based on 179 reviews, and has 1619 subscribers.

You will learn about 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 This course is ideal for individuals who are The Learning Path is for machine learning engineers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R It is particularly useful for The Learning Path is for machine learning engineers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R.

Enroll now: Learning Path: R: Complete Machine Learning & Deep Learning

Summary

Title: Learning Path: R: Complete Machine Learning & Deep Learning

Price: $44.99

Average Rating: 4.45

Number of Lectures: 213

Number of Published Lectures: 213

Number of Curriculum Items: 213

Number of Published Curriculum Objects: 213

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

The Learning Path is for machine learning engineers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R

Target Audiences

The Learning Path is for machine learning engineers, statisticians, and data scientists who want to create cutting-edge machine learning and deep learning models using R

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 techniquesand 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.

Course Curriculum

Chapter 1: Mastering R Programming

Lecture 1: The Course Overview

Lecture 2: Performing Univariate Analysis

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

Lecture 4: Detecting and Treating Outlier

Lecture 5: Treating Missing Values with `mice`

Lecture 6: Building Linear Regressors

Lecture 7: Interpreting Regression Results and Interactions Terms

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

Lecture 9: Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA

Lecture 10: Validating Model Performance on New Data with k-Fold Cross Validation

Lecture 11: Building Non-Linear Regressors with Splines and GAMs

Lecture 12: Building Logistic Regressors, Evaluation Metrics, and ROC Curve

Lecture 13: Understanding the Concept and Building Naive Bayes Classifier

Lecture 14: Building k-Nearest Neighbors Classifier

Lecture 15: Building Tree Based Models Using RPart, cTree, and C5.0

Lecture 16: Building Predictive Models with the caret Package

Lecture 17: Selecting Important Features with RFE, varImp, and Boruta

Lecture 18: Building Classifiers with Support Vector Machines

Lecture 19: Understanding Bagging and Building Random Forest Classifier

Lecture 20: Implementing Stochastic Gradient Boosting with GBM

Lecture 21: Regularization with Ridge, Lasso, and Elasticnet

Lecture 22: Building Classifiers and Regressors with XGBoost

Lecture 23: Dimensionality Reduction with Principal Component Analysis

Lecture 24: Clustering with k-means and Principal Components

Lecture 25: Determining Optimum Number of Clusters

Lecture 26: Understanding and Implementing Hierarchical Clustering

Lecture 27: Clustering with Affinity Propagation

Lecture 28: Building Recommendation Engines

Lecture 29: Understanding the Components of a Time Series, and the xts Package

Lecture 30: Stationarity, De-Trend, and De-Seasonalize

Lecture 31: Understanding the Significance of Lags, ACF, PACF, and CCF

Lecture 32: Forecasting with Moving Average and Exponential Smoothing

Lecture 33: Forecasting with Double Exponential and Holt Winters

Lecture 34: Forecasting with ARIMA Modelling

Lecture 35: Scraping Web Pages and Processing Texts

Lecture 36: Corpus, TDM, TF-IDF, and Word Cloud

Lecture 37: Cosine Similarity and Latent Semantic Analysis

Lecture 38: Extracting Topics with Latent Dirichlet Allocation

Lecture 39: Sentiment Scoring with tidytext and Syuzhet

Lecture 40: Classifying Texts with RTextTools

Lecture 41: Building a Basic ggplot2 and Customizing the Aesthetics and Themes

Lecture 42: Manipulating Legend, AddingText, and Annotation

Lecture 43: Drawing Multiple Plots with Faceting and Changing Layouts

Lecture 44: Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots

Lecture 45: ggplot2 Extensions and ggplotly

Lecture 46: Implementing Best Practices to Speed Up R Code

Lecture 47: Implementing Parallel Computing with doParallel and foreach

Lecture 48: Writing Readable and Fast R Code with Pipes and DPlyR

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

Lecture 50: Interface C++ in R with RCpp

Lecture 51: Understanding the Structure of an R Package

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

Lecture 53: Performing Important Checks Before Submitting to CRAN

Lecture 54: Submitting an R Package to CRAN

Chapter 2: R Machine Learning solutions

Lecture 1: The Course Overview

Lecture 2: Downloading and Installing R

Lecture 3: Downloading and Installing RStudio

Lecture 4: Installing and Loading Packages

Lecture 5: Reading and Writing Data

Lecture 6: Using R to Manipulate Data

Lecture 7: Applying Basic Statistics

Lecture 8: Visualizing Data

Lecture 9: Getting a Dataset for Machine Learning

Lecture 10: Reading a Titanic Dataset from a CSV File

Lecture 11: Converting Types on Character Variables

Lecture 12: Detecting Missing Values

Lecture 13: Imputing Missing Values

Lecture 14: Exploring and Visualizing Data

Lecture 15: Predicting Passenger Survival with a Decision Tree

Lecture 16: Validating the Power of Prediction with a Confusion Matrix

Lecture 17: Assessing performance with the ROC curve

Lecture 18: Understanding Data Sampling in R

Lecture 19: Operating a Probability Distribution in R

Lecture 20: Working with Univariate Descriptive Statistics in R

Lecture 21: Performing Correlations and Multivariate Analysis

Lecture 22: Operating Linear Regression and Multivariate Analysis

Lecture 23: Conducting an Exact Binomial Test

Lecture 24: Performing Student's t-test

Lecture 25: Performing the Kolmogorov-Smirnov Test

Lecture 26: Understanding the Wilcoxon Rank Sum and Signed Rank Test

Lecture 27: Working with Pearson's Chi-Squared Test

Lecture 28: Conducting a One-Way ANOVA

Lecture 29: Performing a Two-Way ANOVA

Lecture 30: Fitting a Linear Regression Model with lm

Lecture 31: Summarizing Linear Model Fits

Lecture 32: Using Linear Regression to Predict Unknown Values

Lecture 33: Generating a Diagnostic Plot of a Fitted Model

Lecture 34: Fitting a Polynomial Regression Model with lm

Lecture 35: Fitting a Robust Linear Regression Model with rlm

Lecture 36: Studying a case of linear regression on SLID data

Lecture 37: Reducing Dimensions with SVD

Lecture 38: Applying the Poisson model for Generalized Linear Regression

Lecture 39: Applying the Binomial Model for Generalized Linear Regression

Lecture 40: Fitting a Generalized Additive Model to Data

Lecture 41: Visualizing a Generalized Additive Model

Lecture 42: Diagnosing a Generalized Additive Model

Lecture 43: Preparing the Training and Testing Datasets

Lecture 44: Building a Classification Model with Recursive Partitioning Trees

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 5 votes
2 stars: 17 votes
3 stars: 32 votes
4 stars: 59 votes
5 stars: 66 votes

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