/Python_For_AIML_DataScience

This Repository is for learning python needed for AI and Machine Learning and Data Science - This is one stop repo for anything related to python for AI , ML and Data Science. Feel Free to Contribute to it

Primary LanguageJupyter Notebook

Python_For_AIML_DataScience

This Repository is for learning python needed for AI and Machine Learning and Data Science - This is one stop repo for anything related to python for AI , ML and Data Science. Feel Free to Contribute to it

Topics Covered

1_Python Basics

  • 1_1_Introduction/ (Basics of Python, syntax, interpreter)
  • 1_2_ControlStructures/ (if-else, loops, etc.)
  • 1_3_Functions/ (Function definitions, arguments, return values)
  • 1_4_ModulesAndPackages/ (Importing modules, exploring standard libraries)
  • 1_5_FileIO/ (Reading and writing files)
  • 1_6_ExceptionHandling/ (try-except blocks, raising exceptions)

2_Python Data Structures

  • 2_1_ListsAndTuples/ (Creation, manipulation, and usage)
  • 2_2_Dictionaries/ (Key-value pairs, dictionary methods)
  • 2_3_Sets/ (Set operations and methods)
  • 2_4_StringManipulation/ (String methods, formatting)
  • 2_5_AdvancedDataStructures/ (Collections module, data structures like heap, queue)

3_Python OOP

  • 3_1_ClassesAndObjects/ (Defining classes, creating objects)
  • 3_2_Inheritance/ (Deriving classes, method overriding)
  • 3_3_Polymorphism/ (Concepts, practical applications)
  • 3_4_Encapsulation/ (Private and protected members, getters and setters)
  • 3_5_AdvancedOOPConcepts/ (Metaclasses, decorators, class and static methods)

4_Data Manipulation and Analysis

4_1_Pandas/

  • 4_1_1_Basics/ (DataFrame creation, basic operations)
  • 4_1_2_DataCleaning/ (Handling missing data, data transformation)
  • 4_1_3_DataTransformation/ (Grouping, merging, pivot tables)
  • 4_1_4_AdvancedPandasTechniques/ (Time series, categorical data)

4_2_NumPy/

  • 4_2_1_Basics/ (Array creation, basic array operations)
  • 4_2_2_ArrayOperations/ (Indexing, slicing, reshaping arrays)
  • 4_2_3_LinearAlgebra/ (Matrix operations, eigenvalues)
  • 4_2_4_StatisticalFunctions/ (Descriptive statistics, random sampling)

5_Data Visualization

5_1_Matplotlib/

  • 5_1_1_Basics/ (Plot types, labels, legends)
  • 5_1_2_AdvancedPlots/ (Histograms, scatter plots, 3D plots)
  • 5_1_3_Customizations/ (Styling, themes, annotations)

5_2_Seaborn/

  • 5_2_1_Basics/ (Data visualization using Seaborn)
  • 5_2_2_StatisticalPlots/ (Box plots, violin plots)
  • 5_2_3_ThemesAndStyles/ (Customizing plots with Seaborn)

5_3_InteractivePlots/

  • 5_3_1_Bokeh/ (Interactive plotting with Bokeh)
  • 5_3_2_Plotly/ (Creating interactive plots with Plotly)

6_Machine Learning

6_1_scikit-learn/

  • 6_1_1_SupervisedLearning/ (Classification, regression)
  • 6_1_2_UnsupervisedLearning/ (Clustering, dimensionality reduction)
  • 6_1_3_ModelEvaluation/ (Cross-validation, performance metrics)
  • 6_1_4_FeatureEngineering/ (Feature selection, feature extraction)

6_2_ModelSelection/ (Comparing, selecting, and fine-tuning ML models)

6_3_HyperparameterTuning/ (Grid search, randomized search)

7_Deep Learning and Neural Networks

7_1_TensorFlow/

  • 7_1_1_Basics/ (Introduction to TensorFlow, basic operations)
  • 7_1_2_Models/ (Building and training neural network models)
  • 7_1_3_AdvancedTechniques/ (Custom layers, loss functions, optimizers)

7_2_PyTorch/

  • 7_2_1_Basics/ (Fundamentals of PyTorch)
  • 7_2_2_Models/ (Neural network design and implementation)
  • 7_2_3_AdvancedTechniques/ (Autograd, dynamic computation graphs)

7_3_Keras/

  • 7_3_1_Basics/ (Keras for building and training models)
  • 7_3_2_Models/ (Sequential and functional API)
  • 7_3_3_AdvancedTechniques/ (Custom callbacks, layers, and training loops)

8_Scientific Computing

8_1_SciPy/

  • 8_1_1_Basics/ (Scientific computing with SciPy)
  • 8_1_2_Optimization/ (Optimization algorithms)
  • 8_1_3_Integration/ (Numerical integration techniques)
  • 8_1_4_Interpolation/ (Data interpolation and smoothing)

8_2_SymPy/

  • 8_2_1_Basics/ (Symbolic mathematics in Python)
  • 8_2_2_Algebra/ (Solving equations, algebraic manipulations)
  • 8_2_3_Calculus/ (Differentiation, integration, series expansion)

8_3_Others/

  • 8_3_1_NetworkX/ (Graph theory in Python)
  • 8_3_2_NLTK/ (Natural Language Processing with Python)

9_Statistics and Mathematics

  • 9_1_Probability/ (Basics of probability theory) `

  • 9_2_StatisticalTesting/ (Hypothesis testing, p-values, confidence intervals)

  • 9_3_LinearAlgebra/ (Matrix operations, vector spaces, eigenvalues)

  • 9_4_Calculus/ (Limits, derivatives, integrals, multivariable calculus)

  • 9_5_DiscreteMathematics/ (Logic, set theory, combinatorics)