Python to Artificial Intelligence

On this Github, I cover everything from Python to Artificial Intelligence with different Python, Machine Learning, and Deep Learning programs.

Theoretical concepts are covered on the Github Wiki with practical codes available on in the repository. For detailed explanations, videos are available on my YouTube channel. For more tutorials, check out my articles on Medium.

Topics Covered

>> Python

1. Fundamentals

   • Introduction to the environment of Python
   • Variables & Expressions
   • Operators
   • Input/output statements
   • Conditions & Branching: if, if-else, nested-if
   • Loops: definite, indefinite using while & for
   • Control statements: break & continue
   • Functions, Usage of range function

2. Python Data Structures

   • Mutable & immutable data types
   • Lists: accessing list, operations, functions & methods, slicing of list, manipulation
   • Strings: operations, functions & methods, slicing of strings
   • Tuples: accessing tuples, operations, functions & methods
   • Dictionaries: accessing members, working with dictionaries
   • Sets

3. Python Advanced Concepts

   • Python Modules, Packages
   • File handling: writing data to files and reading data from files
   • OOPs concepts: classes, constructors, inheritance, overloading

4. Data Analysis using Pandas

   • Panda data structure: series, data-frame, panel
   • Different methods of creating data-frames
   • Working with different file formats (csv, excel, txt, json)
   • Different methods of indexing and selecting data
   • Applying aggregation on data frames
   • Different methods of wrangling of data
   • Visualization of data

5. NumPy

   • Introduction to NumPy,original python vs. NumPy
   • Arrays: array creation of 0-D, 1-D, 2-D, Multidimensional arrays
   • Inspection
   • NumPy operations
   • Data types
   • Functions & methods
   • Indexing and slicing

>> Artificial Intelligence

1. Scope of AI & Problem Solving

   • Introduction to Artificial Intelligence
   • Applications - Games, Theorem proving, Natural language processing, Vision and speech processing, Robotics, Expert systems
   • AI techniques - search knowledge, Abstraction
   • State space search, Production systems
   • Search space control: depth-first, breadth-first search
   • Heuristic search - Hill climbing
   • Best-first search, branch and bound
   • Problem Reduction, Constraint Satisfaction End
   • Means-End Analysis

2. Expert systems

   • Expert System: Need
   • Justification for expert systems
   • Knowledge acquisition
   • Learning: Concept of learning
   • Learning automation
   • Learning by inductions
   • Handling Uncertainties: Non-monotonic reasoning, Probabilistic reasoning
   • Use of certainty factors

3. Fuzzy logic

   • Basic concepts
   • Fuzzy sets and Crisp sets
   • Fuzzy set theory and operations
   • Properties of fuzzy sets
   • Fuzzy relations, rules, propositions, implications and inferences
   • Defuzzification techniques
   • Fuzzy logic controller design
   • Applications of Fuzzy logic

>> Machine Learning

>> Deep Learning

1. Computer Vision
2. Natural Language Processing

>> Recommender Systems

References

• To learn more about Artificial Intelligence, see Artificial Intelligence, Machine Learning, and Deep Learning.
• Github Wiki
• YouTube channel