Artificial Intelligence Full Course

Introduction

• Host: Zulaikha from Edureka.
• Covers domains and concepts under AI, use cases, practical implementations using Python.
• Agenda includes history, applications, programming languages, machine learning, deep learning, and natural language processing.

History of Artificial Intelligence

• Classical Ages: Concepts of mechanical men in Greek mythology (e.g., Talos).
• 1950: Alan Turing published paper on machine thinking, created the Turing Test.
• 1951: Game AI, checkers, and chess programs developed.
• 1956: John McCarthy coined 'Artificial Intelligence' at the Dartmouth Conference.
• 1960s-1990s: Development of AI labs, robots, chatbots, Deep Blue beating Garry Kasparov.
• 2000s-Present: AI in DARPA Grand Challenge, IBM Watson, AI in everyday applications.

Why AI is Popular Now

• Increased computational power (e.g., GPUs).
• Vast amounts of data generation (social media, IoT).
• Better algorithms, especially neural networks.
• Investment and belief in AI as the future by companies like Google, Amazon, and Facebook.

What is Artificial Intelligence?

• Coined in 1956 by John McCarthy as making intelligent machines.
• AI systems perform tasks requiring human intelligence (visual perception, decision making).
• Applications include predictive search engines, finance (JP Morgan's Contract Intelligence), healthcare (IBM Watson).

Types of Artificial Intelligence

1. Artificial Narrow Intelligence (ANI): Specialized for specific tasks (e.g., Alexa, Google search).
2. Artificial General Intelligence (AGI): Machines with human-like intelligence (not yet achieved).
3. Artificial Super Intelligence (ASI): Hypothetical future where machines surpass human intelligence.

Programming Languages for AI

• Python: Most preferred for its simplicity and extensive libraries.
• R: Statistical programming for data analysis.
• Java: Good for search algorithms and neural networks.
• Lisp: Oldest AI language, suited for symbolic processing.
• Prolog: Used in expert systems and knowledge bases.
• Other Languages: C++, Julia, MATLAB, JavaScript, SaaS.

Machine Learning

• Definition: Subset of AI focusing on data-driven machine learning.
• Need: Handles large volumes of data, improves decision-making, uncovers data patterns, solves complex problems.
• Types: Supervised, unsupervised, reinforcement learning.
• Algorithms: Linear regression, logistic regression, decision trees, random forests, SVMs, k-nearest neighbors.

Deep Learning

• Introduction: Handles high-dimensional data, automates feature extraction.
• Neural Networks: Inspired by human brain, consists of neurons (perceptrons).
• Types: Feedforward networks, convolutional networks, recurrent networks.
• Backpropagation: Training method to update weights, minimizing error.
• Limitations: High computational resources, complex model training.

Natural Language Processing (NLP)

• Definition: Enables computers to understand human language.
• Applications: Sentiment analysis, chatbots, speech recognition, machine translation.
• Concepts: Tokenization, stemming, lemmatization, stop words, document term matrix.
• Demo: Sentiment analysis using Naive Bayes.

Conclusion

• AI is an integral part of modern technology, influencing various sectors.
• Edureka offers a comprehensive machine learning engineer's program.
• Encouragement to subscribe to Edureka for more learning resources.

Recommended Actions

• Review AI concepts regularly.
• Practice programming and model implementation using Python.
• Explore Edureka's courses for in-depth learning.