Artificial Intelligence Full Course Lecture Notes

Introduction

• Speaker: Zulaikha from Edureka
• Overview of today's session: covering domains, concepts of AI, use cases, and practical implementations using Python.
• Agenda:
  • History of AI
  • Current relevance of AI
  • Definition of AI
  • Applications of AI
  • Basics of AI
  • Programming languages for AI (focus on Python)
  • Introduction to Machine Learning
  • Types of Machine Learning algorithms
  • Limitations of Machine Learning and need for Deep Learning
  • Deep Learning concepts
  • Introduction to Natural Language Processing (NLP)
  • Practical implementation of NLP using Python

History of Artificial Intelligence

• Classical Ages: Concepts of machines in Greek mythology. Example: Talos - a giant animated warrior.
• 1950: Alan Turing's paper introduced the Turing Test.
• 1956: John McCarthy coined the term "Artificial Intelligence" at Dartmouth Conference.
• 1960s: Development milestones in AI, including first robot and chatbot.
• 1997: IBM's Deep Blue defeated chess champion Garry Kasparov.
• 2011: IBM Watson won in Jeopardy.

Current Relevance of AI

• Computation Power: Advances in technology with GPUs enabling complex AI models.
• Data Generation: Massive data generation through IoT and social media necessitating AI for data processing.
• Better Algorithms: Development of effective algorithms based on neural networks.
• Investment: Significant investment in AI from companies like Google, Amazon, Microsoft.

Definition of Artificial Intelligence

• AI is the science and engineering of creating intelligent machines capable of performing tasks requiring human intelligence, such as:
  • Visual perception
  • Speech recognition
  • Decision making
  • Language translation

Applications of Artificial Intelligence

• Examples:
  • Google predictive search
  • Financial analysis in JP Morgan Chase
  • Healthcare AI software (IBM Watson)
  • Self-driving cars (Tesla)
  • Netflix recommendations
  • Email classification in Gmail

Basics of AI

• Types of AI:
  1. Artificial Narrow Intelligence (Weak AI): Specialized applications (e.g., chatbots, search engines).
  2. Artificial General Intelligence (Strong AI): Hypothetical machines capable of human-like intelligence.
  3. Artificial Superintelligence: Machines surpassing human intelligence - still theoretical.

Programming Languages for AI

1. Python: Most popular for AI due to simple syntax and extensive libraries.
2. R: Effective for statistical analysis.
3. Java: Useful for larger projects.
4. Others: Lisp, Prolog, C++, etc.

Machine Learning

• Definition: Subset of AI focused on algorithms that allow machines to learn from data.
• Types of Machine Learning:
  • Supervised Learning: Learning from labeled data.
  • Unsupervised Learning: Learning from unlabeled data.
  • Reinforcement Learning: Learning through interaction with the environment to maximize rewards.
• Algorithms: Classification algorithms include: Linear Regression, Logistic Regression, Decision Trees, Random Forest, Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN).

Limitations of Machine Learning

1. High Dimensionality: Difficulty in processing high dimensional data.
2. Feature Extraction: Requires significant effort to identify important features for model performance.

Deep Learning

• Definition: Subset of machine learning utilizing neural networks to learn from data.
• Neural Networks: Composed of layers (input, hidden, and output), each containing neurons (perceptrons).
• Backpropagation: Key algorithm for training neural networks by updating weights to minimize error.

Natural Language Processing (NLP)

• Definition: Subfield of AI focused on the interaction between computers and human language.
• Applications:
  • Sentiment Analysis
  • Chatbots
  • Machine Translation
  • Text Mining

Key Concepts in NLP

• Tokenization: Breaking text into smaller units, such as words.
• Stemming and Lemmatization: Normalizing words to their base forms.
• Stop Words: Commonly used words that can be excluded from analysis to focus on important terms.
• Document-Term Matrix: Matrix representation of documents in terms of word frequencies.

Practical Implementations

Machine Learning Demo

• Covered classification algorithms like Logistic Regression, Decision Trees, K-NN, Naive Bayes, and SVM.

NLP Demo

• Implemented Sentiment Analysis using NaiveBayesClassifier.

Edureka Machine Learning Engineer Program

• Comprehensive training program covering Python, Machine Learning, Deep Learning, NLP, etc.
• Features: Certification upon completion, hands-on projects, and expert mentorship.

Conclusion

• Importance of AI, ML, Deep Learning, and NLP in modern technology.
• Encouragement to ask questions or seek further clarification in the comments.