Course Description

This course provides comprehensive training on machine learning and deep learning techniques, focusing on practical applications in R. Participants will gain hands-on experience with algorithms, model building, and real-world case studies, equipping them with the skills needed for modern data science roles.

Learning Outcomes

• Understand the fundamentals of machine learning and deep learning.
• Apply supervised and unsupervised learning techniques using R.
• Build and evaluate models using various machine learning algorithms.
• Implement deep learning models, including neural networks, CNNs, and RNNs, in R.
• Use R for practical data analysis, including predictive analytics and image classification.

Course Structure and Content

Day	Topic
Day 1	Introduction to Machine Learning and Data Preprocessing
Day 2	Supervised Learning Algorithms: Linear Regression, Logistic Regression, k-NN
Day 3	Unsupervised Learning and Clustering: k-Means, Hierarchical Clustering, PCA
Day 4	Introduction to Deep Learning: Neural Networks, CNNs, and RNNs
Day 5	Practical Applications in R: End-to-End Machine Learning Pipeline and Projects

Teaching and Learning Strategy

• Lectures with real-world case studies and examples.
• Hands-on coding sessions in R with datasets.
• Interactive group discussions and problem-solving.
• Self-paced learning through additional resources.

Assessment Strategy

• Practical coding exercises and model implementation (30%)
• Conceptual quizzes on machine learning and deep learning principles (20%)
• Final project on applying machine learning and deep learning techniques (50%)
• Participants achieving 70% or higher will receive a Certificate of Completion.

Certification and Career Opportunities

Upon completion, participants will receive a digital and printed certificate from GLOBALSTAT INTELLIGENCE SOLUTIONS. This course is ideal for professionals seeking careers in data science, machine learning, artificial intelligence, and predictive analytics.

Contact Information

Email: train@globalstatsol.com

Website: https://www.globalstatsol.com

Enrolling

Click below to enroll in this course.