Applied Machine Learning and Deep Learning with R
Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R
Tailored for data scientists, analysts, and R enthusiasts, this program provides hands-on experience in building, training, and deploying machine learning and deep learning models using the R programming language.
What You Will Learn:
- Introduction to Machine Learning with R:
- Gain a solid foundation in machine learning concepts using the R programming language.
- Explore supervised and unsupervised learning techniques, understanding the application of algorithms in real-world scenarios.
- Deep Learning Foundations in R:
- Dive into the fundamentals of deep learning, understanding neural networks, activation functions, and backpropagation.
- Explore the architecture of deep neural networks and their applications in image recognition, natural language processing, and more.
- Data Preprocessing and Feature Engineering:
- Learn essential techniques for preprocessing data and engineering features to enhance model performance.
- Understand the importance of data preparation in the success of machine learning and deep learning models.
- Model Building and Evaluation:
- Build and train machine learning and deep learning models using R libraries such as caret and keras.
- Explore model evaluation metrics and techniques to ensure the robustness and accuracy of your models.
- Transfer Learning and Fine-Tuning:
- Understand the concept of transfer learning and how to leverage pre-trained models for new tasks.
- Learn techniques for fine-tuning deep learning models to adapt them to specific domains or datasets.
- Deployment of Models in R:
- Explore methods for deploying machine learning and deep learning models in R for practical applications.
- Understand the integration of models into production environments and the deployment pipeline.
- Real-world Applications and Case Studies:
- Apply your knowledge through hands-on projects and case studies.
- Gain practical experience in solving real-world problems, from image classification to text generation, using applied machine learning and deep learning techniques.
The content is well-organized, progressing from fundamentals to advanced topics seamlessly. The instructors strike the right balance between theory and practical application. The inclusion of real-world case studies was particularly beneficial, allowing me to see the immediate impact of the techniques I learned.
— Emily
The instructors provide clear explanations and practical examples, making complex concepts accessible. The hands-on projects were challenging but incredibly rewarding, giving me confidence in applying machine learning and deep learning techniques using R. The real-world applications and case studies were a highlight.
— Harrison
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