In this section, you learn the foundational concepts of AI and machine learning, distinguishing between
the broader field of AI and the specific techniques that constitute machine learning.
You explored the types of AI systems, focusing on the key differences between narrow AI, which is
specialized and limited to specific tasks, and general AI, which aims to perform any intellectual
task a human can.
Building on this, you delved into machine learning basics, examining various training methods such
as supervised, unsupervised, and reinforcement learning, and understanding how models learn from
data.
Deep learning was introduced as an advanced subset of machine learning, emphasizing neural networks
and their capability to handle large unstructured data.
Generative AI was also covered, highlighting models that can create new content such as images or text,
and transformer models were discussed for their revolutionary impact on natural language processing
and multimodal Multi-Modal tasks.
An insight into natural language processing provided an understanding of how machines interpret and
generate human language, complemented by multimodal models that integrate and process multiple types
of data simultaneously.
The importance of sociotechnical AI systems was underscored, emphasizing the need for cross-disciplinary
collaboration to address ethical, social, and technical challenges.
The historical context and evolution of AI and data science were explored.
Tracing the development of these fields and their growing influence on various sectors.
By the end of this section, you gained a comprehensive understanding of key AI concepts, the structure
and training of machine learning models, and the broader implications of AI systems.
This knowledge equips you with the ability to critically assess and contribute to advancements in AI
and machine learning, ensuring you are well prepared for future studies or professional applications
in this dynamic field.