R- Machine Learning with R Beginner to Expert!- 4-in-1

R: Machine Learning with R – Beginner to Expert!: 4-in-1, available at $44.99, has an average rating of 3.85, with 197 lectures, 1 quizzes, based on 12 reviews, and has 94 subscribers.

You will learn about Process a classic dataset, from data cleaning to presenting results with effective graphics. Evaluate the performance of your models and put your model into use. Explore advanced techniques such as hyper parameter tuning and deep learning. Incorporate R and Hadoop to solve machine learning problems on big data. Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees. Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm. Get to know hyper-parameter tuning by exploring and iterating through parameters This course is ideal for individuals who are An aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, who wants to explore the advanced topics in machine learning with R with examples to build powerful predictive models in R! or Anyone who wants to enter the world of machine learning and is looking for a guide that is easy to follow. It is particularly useful for An aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, who wants to explore the advanced topics in machine learning with R with examples to build powerful predictive models in R! or Anyone who wants to enter the world of machine learning and is looking for a guide that is easy to follow.

Enroll now: R: Machine Learning with R – Beginner to Expert!: 4-in-1

Summary

Title: R: Machine Learning with R – Beginner to Expert!: 4-in-1

Price: $44.99

Average Rating: 3.85

Number of Lectures: 197

Number of Quizzes: 1

Number of Published Lectures: 197

Number of Published Quizzes: 1

Number of Curriculum Items: 198

Number of Published Curriculum Objects: 198

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Process a classic dataset, from data cleaning to presenting results with effective graphics.

Evaluate the performance of your models and put your model into use.

Explore advanced techniques such as hyper parameter tuning and deep learning.

Incorporate R and Hadoop to solve machine learning problems on big data.

Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees.

Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm.

Get to know hyper-parameter tuning by exploring and iterating through parameters

Who Should Attend

An aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, who wants to explore the advanced topics in machine learning with R with examples to build powerful predictive models in R!

Anyone who wants to enter the world of machine learning and is looking for a guide that is easy to follow.

Target Audiences

An aspiring data scientist who is familiar with the basic of the R language, data frames, and some basic knowledge in statistics, who wants to explore the advanced topics in machine learning with R with examples to build powerful predictive models in R!

Anyone who wants to enter the world of machine learning and is looking for a guide that is easy to follow.

Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. R language is widely used among statisticians and data miners to develop statistical software and perform data analysis. It provides a cutting-edge power you need to work with Machine Learning techniques.

This comprehensive 4-in-1 is a step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language. Apply R to simple predictive modeling with short and simple code. Dive into the advanced algorithms such as hyper-parameter tuning and DeepLearning, and putting your models into production!

By the end of this course, you’ll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R!

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Getting Started with Machine Learning in R, covers learning Machine learning techniques in the popular statistical language R. The course will take you through some different types of ML. You’ll work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. You’ll explore algorithms like Random Forest and Naive Bayes for working on your data in R. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then you’ll have a chance to do it yourself on another data set.

By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs.

The second course, Advanced Machine Learning with R, covers advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples. In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data.

The third course, R Machine Learning solutions, covers building powerful predictive models in R. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You’ll then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction.

The fourth course, Applied Machine Learning and Deep Learning with R covers building powerful machine learning and deep learning applications with help of the R programming language and its various packages. In this course, you’ll examine in detail the R software, which is the most popular statistical programming language of recent years.

Explore different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you’ll dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you’ll learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

By the end of this course, you’ll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R.

About the Authors

Phil Rennertis a Principal Research Engineer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challenging technical problems, innovating new techniques where existing ones don’t apply. He is extensively skilled in machine learning, natural language processing, and data mining.

Tim Hoolihancurrently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group. In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Personally, he enjoys working on practice problems on Kaggle .com as well. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.

Yu-Wei, Chiu (David Chiu) is the founder of LargitData Company. He has previously worked for Trend Micro as a software engineer, with the responsibility of building up 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 to data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, a book compiled for Packt Publishing.

Olgun is PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. Also working as Data Scientist.He is so familiar with Big Data technologies like Hadoop, Spark and able to use Hive, Impala. He is a big fan of R. Also he really loves to work with Shiny, SparkR. He has many academic papers and proceedings about applications of statistics on different disciplines. Mr. Olgun really loves statistic and loves to investigate new methods, share his experience with people.

Course Curriculum

Chapter 1: Getting Started with Machine Learning with R

Lecture 1: The Course Overview

Lecture 2: Your R Environment

Lecture 3: Exploring the US Arrests Dataset

Lecture 4: Creating Test and Train Datasets

Lecture 5: Creating a Linear Regression Model

Lecture 6: Scoring on the Test Set

Lecture 7: Plotting the Test Results

Lecture 8: EDA: mtcars

Lecture 9: Working with Factors

Lecture 10: Scaling Data

Lecture 11: Creating a Classification Model

Lecture 12: Advanced Formulas

Lecture 13: Precision, Recall, and F-Score

Lecture 14: Introduction to Caret

Lecture 15: EDA and Preprocessing

Lecture 16: Preparing Test and Train Datasets

Lecture 17: Creating a Model

Lecture 18: Cross Validation

Lecture 19: F-Score

Chapter 2: Advanced Machine Learning with R

Lecture 1: The Course Overview

Lecture 2: Explore Sonar Data Set

Lecture 3: Tuning Grids

Lecture 4: Iterating – Improving our Tuning

Lecture 5: Final Results

Lecture 6: Neural Networks Basics

Lecture 7: Explore the DNA Set

Lecture 8: Implement a Neural Network

Lecture 9: Multi-layer Perceptron

Lecture 10: One Hot Encoding and MLP

Lecture 11: Overview of the Keras

Lecture 12: Installing Keras

Lecture 13: Neural Network in Keras

Lecture 14: CIFAR10 Data Set

Lecture 15: Convolutional Neural Network

Lecture 16: Saving Your Model in R

Lecture 17: Saving Your Model for Another Language

Lecture 18: Shiny Web Interfaces

Lecture 19: Wrapping Your Model in Shiny

Chapter 3: 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 Students t-test

Lecture 25: Performing the Kolmogorov-Smirnov Test

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

Lecture 27: Working with Pearsons 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: Applying the Gaussian Model for Generalized Linear Regression

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

Lecture 45: Visualizing a Recursive Partitioning Tree

Lecture 46: Measuring the Prediction Performance of a Recursive Partitioning Tree

Lecture 47: Pruning a Recursive Partitioning Tree

Lecture 48: Building a Classification Model with a Conditional Inference Tree

Lecture 49: Visualizing a Conditional Inference Tree

Lecture 50: Measuring the Prediction Performance of a Conditional Inference Tree

Lecture 51: Classifying Data with the K-Nearest Neighbor Classifier

Lecture 52: Classifying Data with Logistic Regression

Lecture 53: Classifying data with the Naīve Bayes Classifier

Lecture 54: Classifying Data with a Support Vector Machine

Lecture 55: Choosing the Cost of an SVM

Lecture 56: Visualizing an SVM Fit

Lecture 57: Predicting Labels Based on a Model Trained by an SVM

Lecture 58: Tuning an SVM

Lecture 59: Training a Neural Network with neuralnet

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 2 votes

4 stars: 4 votes

5 stars: 4 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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