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Janani Ravi, Brandon Strain, Chase DeHan | Duration: 21 h | Video: H264 1280x720 | Audio: AAC 48 kHz 2ch | 2,45 GB | Language: English
This skill will explore advanced mathematical and statistical models and their implementation in the R language. These modeling approaches are relevant to machine learning and in categorizing algorithms.
What You Will Learn
• Implementation of Numerical Methods with R
• Understanding Linear Algebra implementation with R
• Applying Mathematical mass balance Models( Integration, steady state, least square)
• Applying Differential equations problems
• Build Linear inverse models with R
Courses in this path
A: Beginner
Understand the Conceptual framework of Statistical and Mathematical Models.
A1. Understanding Statistical Models and Mathematical Models (Janani Ravi, 2019)
A2. Solving Problems with Numerical Methods (Janani Ravi, 2020)
A3. Binomial Coefficient Analysis with R (Deepika Singh, 2019) | Guide
A4. Beta and Gamma Function Implementation in R (Deepika Singh, 2020) | Guide
B: Intermediate
Implement Dimension Analysis, Differential Equations, Linear Algebra and Mathematical MASS models using R.
B1. Applying the Mathematical MASS Model with R (Janani Ravi, 2020)
B2. Applying Differential Equations and Inverse Models with R (Janani Ravi, 2020)
B3. Performing Dimension Analysis with R (Janani Ravi, 2020)
B4. Applying Linear Algebra with R (Brandon Strain, 2020)
C: Advanced
Learn how to create statistical summaries and implement Monte Carlo Method using R.
C1. Building Statistical Summaries with R (Janani Ravi, 2019)
C2. Implementing Bootstrap Methods in R (Janani Ravi, 2020)
C3. Implementing Monte Carlo Method in R (Chase DeHan, 2020)
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