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Data Mining & Machine Learning with R

Data Mining & Machine Learning with R

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.

What will you learn?

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.

Requirements:

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.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

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.

1.797€*
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Level:
intermediate
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Duration:
7
Hours (days:
1
)
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Training customized to your needs
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Immersive hands-on experience in a dedicated setting
*Price can range depending on number of participants, change of outline, location etc.

Master new skills guided by experienced instructors from anywhere.

1.062€*
Graph Icon - Education X Webflow Template
Level:
intermediate
Clock Icon - Education X Webflow Template
Duration:
7
Hours (days:
1
)
Camera Icon - Education X Webflow Template
Training customized to your needs
Star Icon - Education X Webflow Template
Reduced training costs
*Price can range depending on number of participants, change of outline, location etc.