What you'll learn

Skills you'll gain

There are 3 modules in this course

The course covers fundamental machine learning principles, including supervised and unsupervised learning, model training, evaluation metrics, and optimization techniques. It also provides insights into data preprocessing, feature engineering, and common machine learning algorithms. This course is structured into three modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: ML Basics and Data Preprocessing Module 2: Supervised Learning & Model Evaluation Module 3: Unsupervised Learning, Advanced Techniques & GPU Acceleration By the end of this course, a learner will be able to: - Understand the fundamentals of AI, ML, and Deep Learning, and their key differences. - Implement supervised learning techniques like classification and regression. - Apply clustering methods and time series analysis using ARIMA. - Leverage NVIDIA RAPIDS for GPU-accelerated ML workflows. This course is intended for individuals looking to enhance their machine-learning skills, particularly those interested in GPU-accelerated AI workflows and NVIDIA technologies.

Supervised Learning & Model Evaluation

Unsupervised Learning, Advanced Techniques & GPU Acceleration

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