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47 lines
2.0 KiB
Markdown
47 lines
2.0 KiB
Markdown
# Introduction
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Building an AI system is a careful process of reverse-engineering human traits
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and capabilities in a machine[^1]:
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- Machine Learning: ML teaches a machine how to make inferences and decisions
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based on past experience. It identifies patterns and analyses past data to infer
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the meaning of these data points to reach a possible conclusion without having
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to involve human experience.
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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![](aiVsML.png)
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![](diff.png)
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# Sillabus
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1. Introduction to Python programming and Google Colab
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2. A Landscape to machine learning: learning from real data
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3. Linear and Logistic Regressors
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4. Artificial Neural Networks
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# Introduction to python
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# Landscape to machine learning
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# Linear Regressor
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# Logistic Regressor
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# Artificial Neural Network
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by Gerardo Marx June 2023
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[^1]: https://www.mygreatlearning.com/blog/what-is-artificial- intelligence/
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