Skip to main content

OOPs Concept

OOPs:  stands for Object-Oriented Programming. From this full form we can understand the programming language is focused on 'Objects'. Obejct which is created by class and method. OOP mainly used to binding the data and functions that operate on and no other type of code can access this data except that function also focus on create just codes that don't need to repeat again and again programming. Python classes provide all the standard features of Object Oriented Programming 

OOPs Concepts features are:
  • Class
  • Object
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Message Passing


Comments

Post a Comment

Popular posts from this blog

Classification & Confusion Matrix & Accuracy Paradox

Classification  work on voting the object belongs from which classes has more probability  There are two types of classification : Binary classification : There are two classes we have ex: male-female , cat-dog , yes-not  Multiple classification :   There are classes more than two we have ex: traffic signs , face recognition , flower race  , Digit Recognition Confusion matrix :  Confusion matrix is one type of technique to evaluate the model accuracy for classification problem. In this technique we consider how many of positive and negative data points we predict correctly. The main consideration terms are accuracy, precision and recall The accuracy was an appealing matric, because it was a single number. Here precision and recall(sensitivity) are two numbers. So to get the final score (accuracy) of our model we use F1 score, so that we have a single number. Here is the F1 score's mathematical formula: F1 = 2x precision x recall / (precision ...
Post a Comment
Read more

Multiple classification from many of directories

  # %%  Import nessacary libraries import  numpy  as  np import  pandas  as  pd import  cv2 import  matplotlib.pyplot  as  plt import  os import  glob # %%   Keras Tensorflow libraries from  keras  import  layers from  keras.models  import  Model from  keras.optimizers  import  RMSprop , Adam , Nadam from  keras.preprocessing.image  import  ImageDataGenerator from  keras.layers  import  Input, BatchNormalization, Dense, Dropout, Conv2D, Flatten, GlobalAveragePooling2D, LeakyReLU from  keras.preprocessing.image  import  ImageDataGenerator, img_to_array, load_img # %%  Path path  =   r 'G:/Machine Learning/Project/Lego Mnifigures Classification/dataset' open_dir  =  os....
Post a Comment
Read more

Digit Recognition

Here you can import digit dataset from scikit learn library which is in-built, So you don't need to download from other else Note: If you use visual code, I recommend you to turn your color theme to Monokai because it has a few extra and important keyword and attractive colors than other theme.   # %%  Import libraries import  numpy  as  np import  pandas  as  pd import  matplotlib.pyplot  as  plt import  random  # %%   Load dataset from  sklearn.datasets  import  load_digits dataset  =  load_digits() dataset.keys() output: d ict_keys(['data', 'target', 'target_names', 'images', 'DESCR']) You have to check all to direct print them Here DESCR is a description of dataset # %%   divide the dataset into input and target inputs  =  dataset.data target  =  dataset.target # %% ...
Post a Comment
Read more