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Unlock the combined power of Data Mining & Machine Learning using R in our intensive training. Dive deep into analytical techniques, leveraging R's robust packages and frameworks. Designed for data scientists and analysts, this course offers a comprehensive walkthrough from basic regression to advanced multidimensional reduction techniques.
Advance Your Skills in Data Mining & Machine Learning with R. Over this 2-day intensive course, participants will:
• Embrace R’s Capabilities: Recognize R as a premier tool for statistical computing, data analysis, and visualization.
• Grasp Core Concepts: Differentiate between statistical learning and machine learning, understand the bias-variance trade-off, and more.
• Master Supervised Learning: Dive into techniques from linear regression to decision trees, enhancing predictive modeling skills.
• Explore Unsupervised Learning: Understand the intricacies of clustering, its challenges, and explore methods beyond K-means.
• Dive into Advanced Topics: Enhance predictions with ensemble models, boosting, and dive into dimensionality reduction techniques.By the end of this course, participants will have a solid foundation in both the theoretical and practical applications of data mining and machine learning using R.
Data Scientist Background: This course is tailored for those with prior knowledge in the Data Scientist skill set, particularly within the domain of Analytical Techniques and Methods.
*We customize the course outline and content to your specific needs and relevant use cases.
1. Introduction to Data Mining & Machine Learning
• Distinguishing Statistical Learning from Machine Learning
• Essentials of Iteration and Evaluation
• Navigating the Bias-Variance Trade-off
2. Regression Techniques
• Fundamentals of Linear Regression
• Exploring Generalizations and Non-linearities
• Hands-on Exercises
3. Mastering Classification
• Refresher on Bayesian Principles
• Techniques: Naive Bayes to Neural Networks
• Discriminant Analysis, Logistic Regression, and More
• In-depth Exploration of Support Vector Machines and Decision Trees
• Practical Exercises
4. Cross-validation and Resampling
• Deep Dive into Cross-validation Techniques
• Exploring the Bootstrap Method
• Skill-building Exercises
5. Unsupervised Learning Adventures
• Introduction to K-means Clustering
• Delving into Real-world Examples
• Challenges and Advanced Techniques Beyond K-means
6. Advanced Modeling Topics
• Unraveling Ensemble and Mixed Models
• Techniques in Boosting
• Practical Application Examples
7. Exploring Multidimensional Reduction
• Introduction to Factor Analysis
• Delve into Principal Component Analysis
• Hands-on Analytical Examples.
Hands-on learning with expert instructors at your location for organizations.
Master new skills guided by experienced instructors from anywhere.
