This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, learners will explore machine learning, data science, and generative AI using Python. You will gain a solid understanding of machine learning principles, including supervised and unsupervised learning techniques, and apply algorithms such as linear regression, decision trees, and support vector machines. The course delves into advanced AI concepts like GANs, Variational Autoencoders, and Transformer architecture, which powers models like GPT. You’ll develop, train, and fine-tune models to solve real-world problems such as recommendation systems and sentiment analysis. By the end of the course, you’ll have hands-on experience with tools like TensorFlow, Keras, and Apache Spark to handle large-scale data challenges. You’ll also learn to deploy models in real-time systems while considering AI ethics. This course is ideal for those eager to explore data science, machine learning, and AI. Whether you're a data scientist, developer, or enthusiast, you'll gain the practical skills needed to thrive in the rapidly evolving AI field.
What you'll learn
Understand machine learning principles
Apply supervised and unsupervised learning techniques
Implement algorithms like linear regression and decision trees
Develop and fine-tune models using TensorFlow and Keras
Address real-world problems such as sentiment analysis
Course objectives
Provide a thorough understanding of machine learning and data science
Equip learners with practical skills for AI applications
Encourage ethical considerations in AI model deployment