Course Description
This foundational course provides an in-depth introduction to Artificial Intelligence (AI) and Machine Learning (ML), designed for beginners who want to understand the core concepts and applications of these cutting-edge technologies. The course covers key topics such as supervised and unsupervised learning, data preprocessing, feature engineering, and building predictive models, while offering hands-on practice with popular tools and frameworks like Python, TensorFlow, and scikit-learn.
Prerequisites
- Basic knowledge of programming (preferably Python).
- Understanding of high school-level mathematics, including algebra and probability.
- A passion for learning about AI and its real-world applications.
Key Learning Outcomes
By the end of the course, participants will be able to:
- Understand the fundamental concepts of AI and ML, including their history and current applications.
- Analyze and preprocess data to build effective machine learning models.
- Implement basic supervised and unsupervised learning algorithms.
- Evaluate and optimize machine learning models for better performance.
- Gain familiarity with AI tools, libraries, and frameworks like NumPy, pandas, and TensorFlow.
- Apply AI and ML concepts to solve real-world problems across various domains.
Who Should Attend?
- Students and professionals aspiring to start a career in AI and ML.
- Enthusiasts looking to gain foundational knowledge for advanced studies in AI/ML.
- Individuals interested in leveraging AI for innovative projects or business solutions.
- Teacher: Sajal Saha
