By completing this course, learners will be able to prepare datasets in R, apply statistical and visualization techniques, build regression models, and design, run, and evaluate neural networks. The course begins with data preparation essentials, including working with dataframes, descriptive statistics, and environment setup, ensuring learners can confidently manage their workflow. It then advances to data visualization, where learners generate line graphs, scatter plots, and advanced visualizations to interpret patterns and relationships. Regression modeling concepts are introduced to provide a solid predictive foundation. Finally, the course transitions to deep learning, guiding learners through dataset preparation, neural network coding, multilayer perceptron (MLP) architecture, and predictive testing.
Deep Learning with R: Build & Predict Neural Networks
您将学到什么
Prepare datasets, apply stats, and create visualizations in R.
Build and evaluate regression models for predictive analysis.
Design, run, and test neural networks using R and MLPs.
您将获得的技能
- Plot (Graphics)
- Model Evaluation
- Deep Learning
- Regression Analysis
- Data Visualization Software
- Predictive Analytics
- Data Science
- Statistical Modeling
- Predictive Modeling
- Artificial Neural Networks
- Descriptive Statistics
- Scatter Plots
- Performance Testing
- R Programming
- Statistical Methods
- Data Preprocessing
- Data Manipulation
- 技能部分已折叠。显示 9 项技能，共 17 项。
要了解的详细信息
添加到您的领英档案
13 项作业
October 2025
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
This module introduces learners to the fundamentals of working with R for data science and deep learning projects. Learners will explore dataframes, descriptive statistics, directory setup, variable assignment, and essential R syntax. The module ensures that learners can confidently prepare their environment and datasets before advancing to complex modeling.
涵盖的内容
11个视频4个作业
This module focuses on building strong visualization and regression skills in R. Learners will generate various plots such as line graphs, scatter plots, and multiple plot frames to explore data patterns. The module also introduces regression modeling concepts, including linear and multiple regression, to establish a strong foundation for predictive modeling.
涵盖的内容
9个视频4个作业
This module transitions learners from regression models to deep learning with neural networks in R. It covers preparing datasets, running neural network code, analyzing hidden layers, and evaluating model predictions. By the end of the module, learners will be able to design, execute, and test neural networks for real-world predictive tasks.
涵盖的内容
17个视频5个作业
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