What you'll get
  • 7+ Hours
  • 2 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Gain a comprehensive understanding of deep learning principles and neural network fundamentals.
  • Learn techniques for designing and implementing neural networks in R.
  • Apply strategies to select, optimize, and fine-tune models for better performance.
  • Utilize heuristic algorithms to improve model accuracy and efficiency.
  • Explore practical applications of deep learning in various industries and problem domains.
  • Gain hands-on experience building, training, and evaluating neural network models.
  • Apply best practices for hyperparameter tuning, regularization, and model improvement.
  • Review real-world case studies and examples of AI-driven solutions.
  • Develop skills to address complex challenges and innovate using AI-powered approaches.
  • Utilize deep learning methods to address practical, real-world challenges.
  • Gain exposure to a range of deep learning models, including regression and heuristic-based models.
  • Build a strong foundation in statistics and core deep learning concepts.
  • Acquire knowledge to perform basic statistical operations and run machine learning models in R.
  • Develop an in-depth understanding of data collection, preprocessing, and preparation for machine learning tasks.
  • Learn techniques to translate business problems into actionable machine learning solutions.

Content

Courses No. of Hours Certificates Details
Deep Learning Neural Network with R2h 56m✔View Curriculum
Deep Learning Heuristic using R4h 42m✔View Curriculum

Description

This course offers an in-depth study of neural networks and deep learning in R. As a comprehensive guide, it removes the need for supplementary courses or textbooks on R-based data science. Mastering these techniques in R enables learners to advance their careers and help their organizations gain a competitive edge.
This course is designed for individuals seeking a complete machine learning and deep learning program in R. Upon completion, participants will be able to:
  • Build predictive machine learning and deep learning models in R to solve business challenges and inform strategic decisions.
  • Confidently respond to interview questions on R, machine learning, and deep learning.
  • Compete successfully in online data analytics and data science competitions, including Kaggle.
Deep Learning: Neural Networks with R: Study the fundamentals of neural networks, including architecture, activation functions, and optimization methods. Gain hands-on experience implementing models for classification and regression tasks in R. Learn to build, train, evaluate, and optimize neural networks for optimal performance.
Deep Learning: Heuristics using R: Explore advanced heuristic techniques to improve deep learning model efficiency in R. Study algorithms for model selection, hyperparameter tuning, and performance optimization.
Learners gain practical experience through real-world examples and exercises, developing the skills needed to address complex challenges and deliver high-performing deep learning solutions.

Requirements

  • Those looking to master R and R Studio for data science applications.
  • Learners who already understand fundamental machine learning concepts, including supervised learning.
  • Students who want to apply neural network techniques to real-world datasets in R.
  • Students interested in learning and implementing core deep learning principles in R.

Target Audience

  • People pursuing a professional path in data science.
  • Professionals beginning their careers in data analytics.
  • Statisticians looking for practical experience with data techniques.