This course provides a comprehensive introduction to the basics of Artificial Intelligence (AI) and Machine Learning (ML).

It begins with an overview of AI's historical evolution and its significance across various industries today. Participants will delve into fundamental ML algorithms and explore their applications in modern technologies. The course covers a wide range of ML architectures and models, including supervised and unsupervised learning, transfer learning, NLP, and computer vision. Advanced topics such as neural networks, transformers, and DCC tools like Houdini are also discussed.

Through practical exercises, students will gain hands-on experience in data science fundamentals, including mathematical concepts, Python programming for data manipulation, and performance evaluation using popular libraries like ScikitLearn, TensorFlow, and PyTorch.

The course concludes with a focus on deploying production-ready ML models and understanding the intricacies of machine learning and data pipelines.