Introduction

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine¹:

• 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
3. Linear and Logistic Regressors
4. Artificial Neural Networks

Introduction to python

Landscape to machine learning

Linear Regressor

Logistic Regressor

Artificial Neural Network

by Gerardo Marx June 2023

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1. https://www.mygreatlearning.com/blog/what-is-artificial- intelligence/ ↩︎