Artificial Intelligence Full Course by Edureka

Introduction

• Host: Zulaikha
• Overview of AI domains and concepts
• Use cases and practical implementations with Python

Agenda

1. History of Artificial Intelligence (AI)
2. Why AI is relevant now
3. What is AI?
4. Applications of AI
5. Basics of AI and its types
6. Programming languages for AI
7. Machine Learning (ML)
8. Limitations of ML
9. Deep Learning
10. Introduction to Natural Language Processing (NLP)

History of Artificial Intelligence

• Greek Mythology: Early concepts of mechanical men (e.g., Talos)
• 1950: Alan Turing's paper and Turing Test
• 1951: Game AI - Checkers and Chess programs
• 1956: John McCarthy coined "Artificial Intelligence"
• 1959: First AI lab established at MIT
• 1960-1961: Introduction of first robot in General Motors and chatbot Eliza
• 1997: IBM's Deep Blue beats world chess champion
• 2005: Stanford's car wins DARPA Grand Challenge
• 2011: IBM's Watson in Jeopardy win

Why AI Now?

• Increased computational power (GPUs)
• Massive data generation
• Improved algorithms (neural networks)
• Investment from tech giants and startups

What is AI?

• Defined by John McCarthy
• Science of making intelligent machines
• Machine's ability to perform human-like tasks

Applications of AI

• Predictive Search: Google
• Finance: JP Morgan's Contract Intelligence Platform
• Healthcare: IBM Watson diagnosing leukemia
• Social Media: Facebook's face verification
• Virtual Assistants: Siri, Alexa, Google Duplex
• Self-driving Cars: Tesla
• Entertainment: Netflix recommendation engine

Basics of AI

• Types of AI: Narrow, General, and Super Intelligence
• Programming Languages:
  • Python: Most effective, simple syntax
  • R: Effective for statistical purposes
  • Java: Search algorithms and neural networks
  • Lisp and Prolog: Historical significance
  • Other languages: C++, SaaS, JavaScript, MATLAB, Julia

Machine Learning

• Types: Supervised, Unsupervised, Reinforcement Learning
• Supervised: Labeled data, classification and regression problems
• Unsupervised: Unlabeled data, clustering and association problems
• Reinforcement: Trial and error method with rewards
• Algorithms:
  • Supervised: Linear regression, logistic regression, decision trees, etc.
  • Unsupervised: K-means clustering
  • Reinforcement: Q-learning

Limitations of Machine Learning

• Cannot handle high dimensional data effectively
• Requires manual feature extraction

Deep Learning

• Overcomes ML limitations
• Mimics human brain with neural networks
• Perceptrons: Basic unit of neural networks
• Multi-layer perceptrons: Advanced with hidden layers
• Back-propagation: Training of neural networks
• Types of Networks:
  • Feedforward, Recurrent (RNN), Convolutional (CNN)

Natural Language Processing

• Need: Handling of large unstructured data
• Applications: Sentiment analysis, chatbots, speech recognition
• Key Concepts: Tokenization, stemming, lemmatization, stop words, document-term matrix
• Demo: Sentiment analysis with movie reviews using NaiveBayesClassifier

Conclusion

• Overview of Edureka's machine learning engineer master's program
• Includes comprehensive modules on Python, ML, NLP, Deep Learning, etc.

Additional Resources

• Links to Edureka's courses for further learning and mastery in AI and ML.