Course Outline


Setting up the R Development Environment

Deep Learning vs Neural Network vs Machine Learning

Building an Unsupervised Learning Model

Case Study: Predicting an Outcome Using Existing Data

Preparing Test and Training Data Sets For Analysis

Clustering Data

Classifying Data

Visualizing Data

Evaluating the Performance of a Model

Iterating Through Model Parameters

Hyper-parameter Tuning 

Integrating a Model with a Real-World Application

Deploying a Machine Learning Application


Summary and Conclusion


  • R programming experience
  • An understanding of machine learning concepts
  21 Hours


Related Courses

Introduction to Stable Diffusion for Text-to-Image Generation

  21 hours

Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

  21 hours


  7 hours

TensorFlow Lite for Embedded Linux

  21 hours

TensorFlow Lite for Android

  21 hours

Tensorflow Lite for Microcontrollers

  21 hours

TensorFlow Lite for iOS

  21 hours

Deep Learning Neural Networks with Chainer

  14 hours

Distributed Deep Learning with Horovod

  7 hours

Accelerating Deep Learning with FPGA and OpenVINO

  35 hours

Building Deep Learning Models with Apache MXNet

  21 hours

Deep Learning with Keras

  21 hours

Deep Learning for Self Driving Cars

  21 hours

Advanced Deep Learning with Keras and Python

  14 hours

Deep Learning for Vision with Caffe

  21 hours