This course provides a foundational understanding of machine learning principles, algorithms, and applications. Students will explore the theoretical underpinnings of machine learning, gain hands-on experience with popular algorithms, and learn how to apply these techniques to real-world problems. The course emphasizes both supervised and unsupervised learning approaches, model evaluation, and practical implementation using contemporary tools and libraries.
Learning Objectives:
- Understand the key concepts and terminologies in machine learning.
- Gain proficiency in implementing various machine learning algorithms.
- Develop skills to preprocess data and prepare it for analysis.
- Learn to evaluate and validate machine learning models.
- Explore real-world applications of machine learning and its implications.
- Use popular machine learning libraries and tools for practical projects.
Prerequisites:
- Basic knowledge of programming (preferably in Python)
- Understanding of linear algebra and statistics is helpful but not required
Materials Needed:
- Computer with internet access
- Headphones
Key Outcome:
Upon completing the Machine Learning course, students will acquire a solid understanding of fundamental machine learning principles, including both supervised and unsupervised learning. They will develop practical skills in preprocessing data, implementing various machine learning algorithms, and evaluating model performance using popular tools and libraries.
Assessment:
Assessment will be based on completion of projects, participation in class activities, and a final project showcase.
Certification: Certificate of completion provided upon finishing the course requirements.
Course Duration: 32 credit hours – 4 months
Days & Timings: One-Hour Interactive Class, Twice a Week (Flexible Timings)
Course Format: Normal Track – 2 hours per week, Fast Track – 4 hours per week
Eligible Age Group: 14 and above
Fee: Monthly – USD 120 | Full Course – USD 400