Overview
Delve into the essential algorithms and methodologies at the heart of AI and Machine Learning with this in-depth course. Discover how pre-trained large-language models (LLMs) and various learning paradigms like supervised, unsupervised, and reinforcement learning come together to solve complex business challenges.
Gain hands-on experience in implementing and assessing these techniques, focusing on practical applications and understanding their respective strengths and limitations. Learn to apply feature selection and engineering methods to elevate your model's performance and explore deep learning models tailored for intricate AI tasks.
Throughout the course, you'll be equipped to:
- Implement, evaluate, and articulate the functions of supervised, unsupervised, and reinforcement learning algorithms.
- Enhance model performance with adept feature selection and engineering techniques.
- Articulate deep learning models tailored for multifaceted AI tasks.
- Evaluate the effectiveness of diverse AI & ML methodologies concerning specific business issues.
Prerequisites for success in this course include an intermediate proficiency in Python programming, as well as a foundational understanding of AI and ML, reinforced by familiarity with generative AI (GenAI) and pretrained large-language models (LLM). A basic grasp of statistics will also be beneficial.
Provider: Coursera
Categories: Artificial Intelligence Courses, Machine Learning Courses, Reinforcement Learning Courses, Deep Learning Courses, Neural Networks Courses, Generative AI Courses, Supervised Learning Courses, Unsupervised Learning Courses, Feature Engineering Courses
