Building an AI system is a careful process of reverse-engineering human traits
and capabilities in a machine[^1]:
- Machine Learning: ML teaches a machine how to make inferences and decisions
based on past experience. It identifies patterns and analyses past data to infer
the meaning of these data points to reach a possible conclusion without having
to involve human experience.
- Deep Learning: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
- Neural Networks: Neural Networks work on similar principles to Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
- Natural Language Processing: NLP is the science of reading, understanding, and interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
- Computer Vision: Computer vision algorithms try to understand an image by breaking down an image and studying different parts of the object. This helps the machine classify and learn from a set of images to make a better output decision based on previous observations.
- Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output. Also, take up applications of artificial intelligence courses for free.
# Sillabus
1. Introduction to Python programming and Google Colab
2. A Landscape to machine learning: learning from real data
