AI Powered Machine Learning with R: Practical Applications and Projects
Learning Path | 3 Course Series
Unlock the power of AI-driven machine learning. Master machine learning fundamentals with R. Dive into supervised learning techniques and model evaluation. Apply machine learning to real-world projects using Caret in R. Excel in creating predictive models and analyzing data with R.Learn how to use the R programming language for data science and machine learning and data visualization
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What you'll get
- 25+ Hours
- 3 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Fundamentals of machine learning with R, including data preprocessing and model building.
- Supervised learning techniques such as regression and classification using R.
- Practical application of machine learning concepts to real-world projects using the Caret package in R.
- Techniques for model evaluation, optimization, and performance assessment in R.
- Fundamentals of machine learning with R, including data preprocessing and model building.
- Supervised learning techniques such as regression and classification using R.
- Practical application of machine learning concepts to real-world projects using the Caret package in R.
- Techniques for model evaluation, optimization, and performance assessment in R.
- Be Able To Harness The Power Of R For Practical Data Science
- Read In Data Into The R Environment From Different Sources
- Carry Out Basic Data Pre-processing & Wrangling In R Studio
- Implement Unsupervised/Clustering Techniques Such As k-means Clustering
- Implement Dimensional Reduction Techniques (PCA) & Feature Selection
- Implement Supervised Learning Techniques/Classification Such As Random Forests
- Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Machine Learning with R 20h 25m ✔ Supervised Machine Learning with R 2024 - Linear Regression 3h 05m ✔ Machine Learning Project using Caret in R 1h 58m ✔
Description
This comprehensive course provides participants with a deep understanding of machine learning concepts and techniques using the R programming language. Divided into three sections, the curriculum covers introductory machine learning concepts with R, supervised learning techniques, and practical project implementation using the Caret package. Participants will learn how to apply machine learning algorithms to real-world datasets, evaluate model performance, and create predictive models using R.
Section 1: Machine Learning with R
In this section, participants are introduced to the fundamentals of machine learning using the R programming language. They learn about key machine learning concepts, data preprocessing techniques, model building, and evaluation methods. Through hands-on exercises and projects, participants gain practical experience in implementing machine learning algorithms and analyzing data using R.
Section 2: Supervised Machine Learning with R
Participants delve deeper into supervised learning techniques, focusing on regression and classification algorithms. They learn how to train and evaluate supervised learning models using R, including linear regression, logistic regression, decision trees, random forests, and support vector machines. Participants also explore techniques for feature selection, model optimization, and performance evaluation.
Section 3: Machine Learning Project using Caret in R
In this section, participants apply their machine learning skills to a practical project using the Caret package in R. They learn how to preprocess data, select appropriate machine learning algorithms, tune model parameters, and evaluate model performance using cross-validation techniques. Participants work on a hands-on project, applying their knowledge to solve real-world problems and create predictive models using R.
Throughout the course, participants engage in theoretical lectures, practical demonstrations, and hands-on projects to reinforce their learning and develop proficiency in machine learning with R. By the end of the course, participants will have the knowledge and skills to effectively apply machine learning algorithms to diverse datasets, analyze results, and create predictive models using R.
Requirements
- No prior knowledge required - just be passionate to gain new skills
Target Audience
- R beginners interested in learning R
- data science practitioners who want to deepen their knowledge
- developers who want to learn different aspects of Machine Learning
Course Ratings
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The course covers a wide range of areas into linear and multiple linear regression and decision tress. It also covers both theory and application using R neural network , time series analysis and gradient boosting machines. I have enjoyed learning in this course very much and found it useful in my work.
Tsui Man Kit
This was a really interesting course. I had prior knowledge regarding data analytics, including descriptive, prescriptive and predictive modelling using various tools, but this course was different. This taught me a lot of things which I did not know as a person who was good in statistics and probability. Overall the course was Good.
Keerthi Vasan