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Professional Certification Program
R Language – Use in AI is a structured 8-week program that introduces R programming to M.Tech, M.Sc, and MCA students, as well as professionals in various tech industries. It covers the integration of R in data science, machine learning, deep learning, and natural language processing, providing practical skills and deep insights into R’s use in AI-driven projects.
The course aims to explore the capabilities of the R language in artificial intelligence, equipping participants with the skills to leverage R’s statistical and machine learning capabilities for AI applications.
Section 1.1: Getting Started with R
tidyverse, caret, randomForest, ggplot2.keras, tensorflow, xgboost.Section 1.2: Introduction to Artificial Intelligence and Machine Learning
Section 2.1: Data Collection and Cleaning in R
summary(), str(), head().na.omit(), impute(), etc.).Section 2.2: Feature Engineering and Selection
scale()), normalization, and Min-Max scaling.Section 2.3: Handling Imbalanced Data
Section 3.1: Supervised Learning in R
Section 3.2: Unsupervised Learning in R
Section 3.3: Advanced Machine Learning Models in R
Section 4.1: Introduction to Deep Learning
keras and tensorflow in R.Section 4.2: Building a Neural Network in R
keras.Section 4.3: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Section 5.1: Model Deployment in R
saveRDS(), caret’s train(), and keras models.Section 5.2: Model Optimization and Fine-tuning
caret and mlr libraries for tuning.Take your research to the next level with NanoSchool.
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PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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