Advanced Machine Learning and Deep Learning

This course is part of R Ultimate 2023 - R for Data Science and Machine Learning Specialization

Instructor: Packt - Course Instructors

What you'll learn

  •   Identify and recall deep learning foundations and applications
  •   Explain how to develop and train neural network models
  •   Use techniques to evaluate and optimize model performance
  •   Assess the effectiveness of CNNs for image processing and semantic segmentation
  • Skills you'll gain

  •   Shiny (R Package)
  •   Tensorflow
  •   Unsupervised Learning
  •   Data Processing
  •   Regression Analysis
  •   Deep Learning
  •   Dimensionality Reduction
  •   PyTorch (Machine Learning Library)
  •   Time Series Analysis and Forecasting
  •   Image Analysis
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Machine Learning Methods
  •   Applied Machine Learning
  •   Classification And Regression Tree (CART)
  •   Artificial Neural Networks
  •   Statistical Machine Learning
  •   Predictive Modeling
  •   Interactive Data Visualization
  • There are 8 modules in this course

    Practical aspects include neural network layers, activation functions, and performance metrics in model evaluation. Through hands-on coding labs, you'll cover regression, classification, and convolutional neural networks (CNNs), building and fine-tuning models, understanding loss functions, and using optimizers for accuracy. Emphasis is on frameworks like TensorFlow and PyTorch for developing robust neural networks. The course concludes with specialized topics such as autoencoders, transfer learning, and recurrent neural networks (RNNs). Interactive labs and projects will apply knowledge to complex data analysis, time-series prediction, and creating web applications with Shiny. Ideal for data scientists, machine learning engineers, and AI enthusiasts, prerequisites include Python proficiency and basic machine learning knowledge.

    Deep Learning: Regression

    Deep Learning: Classification

    Deep Learning: Convolutional Neural Networks

    Deep Learning: Autoencoders

    Deep Learning: Transfer Learning and Pretrained Networks

    Deep Learning: Recurrent Neural Networks

    Shiny

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