Machine Learning with R

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

1 day 9 hours 10 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Machine Learning with R provides a thorough introduction to machine learning techniques using the R programming language, focusing on practical applications. You'll gain the skills necessary for preparing data, evaluating models, and applying advanced methods such as ensemble learning and deep learning. This course bridges the gap between theory and real-world applications, ensuring you not only understand the concepts but also know how to implement them in real scenarios. By working with tools like Spark and Hadoop, you will gain experience with big data and develop a comprehensive understanding of the machine learning process. This course stands out by offering a hands-on, interactive approach to mastering machine learning, making it suitable for learners who want to dive into the field. Whether you are just starting out or looking to refine your skills, the course provides a structured learning path to achieve practical, measurable outcomes. By the end of this course, you will be confident in building and deploying machine learning models using R. Ideal for those starting out in data science, this course requires basic knowledge of statistics and programming but does not require prior R experience. It is a perfect fit for learners aiming to enhance their machine learning skills. Based on the book, Machine Learning with R, by Brett Lantz.

Syllabus

Introducing Machine Learning

In this section, we introduce the foundations of machine learning, exploring its origins, core concepts, typical applications, ethical considerations, and practical steps for matching data types to ML algorithms using R.

Managing and Understanding Data

In this section, we manage data using R structures, analyze datasets statistically, and visualize numeric and categorical features for comprehensive data exploration and preparation.

Lazy Learning Classification Using Nearest Neighbors

In this section, we explore lazy learning classification using the k-NN algorithm, measure data similarity with distance metrics, and prepare datasets by normalizing and splitting data for accurate nearest neighbor classification.

Probabilistic Learning Classification Using Naive Bayes

In this section, we explore probabilistic text classification using the Naive Bayes algorithm, covering the fundamentals of probability, conditional probability with Bayes' theorem, and practical SMS spam detection in R.

Divide and Conquer Classification Using Decision Trees and Rules

In this section, we learn how decision trees and rule learners such as C5.0, 1R, and RIPPER divide data for classification, interpret their outputs, and evaluate performance in practical scenarios like loan risk assessment and detecting toxicity.

Forecasting Numeric Data Regression Methods

In this section, we learn to implement regression models-including linear regression and tree-based methods-to estimate numeric outcomes, analyze feature correlations, and apply practical techniques for effective data-driven forecasting.

Black-Box Methods: Neural Networks and Support Vector Machines

In this section, we examine how neural networks and support vector machines (SVMs) model complex data relationships, emphasizing model training, evaluation, and hyperparameter tuning for practical machine learning applications.

Finding Patterns: Market Basket Analysis Using Association Rules

In this section, we apply association rule mining to transactional data, utilize metrics like support and confidence, and implement Apriori and Eclat algorithms to uncover and analyze purchasing patterns for data-driven marketing and inventory strategies.

Finding Groups of Data Clustering with k-means

In this section, we introduce k-means clustering to group unlabeled data, covering concepts of clustering, data preparation, model evaluation, and refinement to uncover actionable patterns in datasets.

Evaluating Model Performance

In this section, we evaluate machine learning models using classification metrics, analyze confusion matrices, and apply validation methods to estimate how the models may perform on future data.

Being Successful with Machine Learning

In this section, we examine the critical factors for successful machine learning, focusing on effective data exploration, project design strategies, and understanding real-world impacts to bridge theory and practical application.

Advanced Data Preparation

In this section, we tackle complex data preparation tasks in R, focusing on combining data sources and feature engineering techniques to support machine learning objectives.

Challenging Data: Too Much, Too Little, Too Complex

In this section, we address challenges in machine learning data by applying feature selection and extraction, handling missing or sparse values with imputation, and using techniques to rebalance imbalanced datasets for improved model performance.

Building Better Learners

In this section, we learn to enhance machine learning models by systematically tuning hyperparameters and applying ensemble methods such as bagging, boosting, and stacking for improved predictive performance.

Making Use of Big Data

In this section, we examine how to apply deep learning models in R using frameworks like Keras and TensorFlow, process large, unstructured data formats, and implement parallel computing for scalable machine learning solutions.