R20-CSE-OOPS through C++-UNIT-4

UNIT-4

welcome to letslearningcse , lets dive into computer science concepts

 

 Title: Unit 4: JNTUK R20 Syllabus-Based Standard Template Library in C++

Overview

One of C++'s most useful features is the Standard Template Library (STL), which offers pre-made templates for a wide range of data structures and algorithms. We'll talk about the STL in C++ programming in Unit 4 of the JNTUK R20 syllabus in this blog article.

Typical Template Collection

The JNTUK R20 syllabus's fourth unit is all about C++ programming's Standard Template Library (STL). With the help of the robust STL feature of C++, programmers may create code that is both optimized and efficient by using pre-defined templates for a variety of data structures and algorithms.

Students study the many STL components in this subject, such as allocators, containers, iterators, and algorithms. They Discover the several kinds of STL containers—vectors, lists, sets, and maps, among others—and how to utilize them for data manipulation and storing.

Data structures called containers are used to store a group of things. Sequence containers, associative containers, and unordered associative containers are among the various kinds of containers that the STL offers. Associative containers sort data according to a key, whereas sequence containers store data in a predetermined order. Data is stored in unsorted associative containers according to a hash function.

Students gain knowledge of the various kinds of sequence containers, including lists, deques, and vectors, as well as how to store and manage data using them. Dynamic arrays with the ability to self-resize are called vectors, whereas deques are queuing systems with two ends that permit items to be added or removed. Lists are linked lists that make it easy to add and remove elements.

Additionally, students study the various forms of associative containers, including sets and maps, and how to store and manage data with them. Maps are collections of key-value pairs, and sets are sorted collections of unique components.

Students also study about iterators, which are utilized to navigate and work with the components of containers. They study the numerous kinds of iterators and discover how to apply them with different containers. These include input, output, forward, and bidirectional iterators.

Pointers to the components of containers are what iterators are. They offer a means of obtaining and change the components of containers without disclosing the implementation below. The use of iterators to navigate between container elements, retrieve their values, and alter them is taught to students.

Students also gain knowledge of the several algorithms offered by the STL, including algorithms for sorting, searching, and manipulation. They study the many kinds of algorithms and learn how to apply them with different containers and iterators. Examples of these are sort, binary_search, and reverse.

Functions that carry out actions on containers and their constituent parts are called algorithms. Numerous algorithms, such as those for sorting, searching, and manipulation, are available in the STL. Students gain knowledge of how to sort, search, and work with the components of containers using these techniques.

Finally, allocators—which are used to manage memory—are taught to the pupils container allocation and deallocation. They study the many kinds of allocators and discover how to apply them with different containers. Examples of these are default and custom allocators.

The objects that control memory allocation and deallocation for containers are called allocators. Most containers use the default allocator provided by the STL. In order to manage memory in a particular fashion, for as by using a memory pool or a custom memory manager, students are taught how to design custom allocators.

Conclusion

The JNTUK R20 syllabus's fourth unit offers a thorough introduction to C++ programming's Standard Template Library (STL). Through the study of the many STL components, including containers, iterators, algorithms, and allocators, students acquire the abilities and information required to create effective and utilized pre-defined templates to optimize the code.

A strong element of C++ is the STL, which lets programmers create intricate and sophisticated software. Students who successfully complete this class will be able to leverage the pre-defined templates provided by the STL to produce streamlined, optimized code—a critical skill for anyone hoping to succeed in the software development industry.

Additionally, the STL offers a method for creating generic code that may be used to various platforms and applications. Developers can save time and effort when creating sophisticated data structures and algorithms from scratch by utilizing the pre-defined templates provided by the STL.

To sum up, the JNTUK R20 syllabus's fourth unit is a crucial component of the C++ programming curriculum. It gives pupils the information and abilities they need to properly and effectively use the STL. Students who successfully complete this course will be able to write optimized and efficient code, which is a prerequisite for a successful career in software development.

Link to the Material: UNIT-4

 

UNIT-3-ml

UNIT-3-ML

Welcome to the letslearningcse blog! today In this post, I came up with some cool and interesting stuff in machine learning that is none other than ensemble learning. According to the JNTU syllabus, ensemble learning is unit 3 of machine learning. 

 

We'll start with a brief introduction to classification and its types.

Classification is nothing but a supervised learning technique that enhances categorical data for its target variable. This kind of target output is called classification.

There are two types of classifications:

• Binary Classification
• Multi-class Classification

Binary Classification: If a target variable contains categorical data with exactly 2 categories, this is known as binary classification.

Multi-class Classification: If a target variable contains categorical data with more than two categories, this is known as multi-class classification. Here we have multiple class labels present in the given dataset. It has two types of mechanisms:

• One vs. All [OVA] or One vs. Rest [OVR]: n-class instances or class labels generate n-binary classifier models. formula: n classes=n classifiers
• One vs. One: n-class instances or labels generate n (n-1)/2 binary classifier models.                    formula:n classes = n(n-1)/2 classifiers

MNIST [Modified National Institute of Standard Technology] Dataset:

• It is a set of 70,000 small images of (0–9) digits handwritten by students and employees of the US Census Bureau.
• This is considered a hello-world program in deep learning, which is a subset of machine learning.
• MNIST is a dataset of handwritten digits, consisting of 70,000 images of size 28x28 pixels.
• The dataset is divided into two sets: 60,000 images for training and 10,000 images for testing purposes.
• The sklearn.dataset package contains three major functions for datasets.
• load: function loads small toy datasets [.csv files] connected to sklearn
• fetch: A function such as fetch_openml() can be used to load real-world datasets.
  
• make: This function generates the fake datasets useful for testing.

Let's get into a brief introduction to ensemble learning. It is nothing but the addition of numerous machine learning modals to provide an efficient method.

It has mainly two types of ensembles:

• Homogeneous ensemble

In this ensemble, it uses the same algorithm for various models.

• Heterogeneous ensemble:

In this type of ensemble technique, different algorithms are used. 

 

Sequential ensemble means the model depends on other models output weighted datasets [error-contained data partitions] and does the processing until the maximum is reached or the output is error-free.

Parallel ensemble means a model independent of other models, and each model predicts the output independently.

 

Ensemble techniques were classified into two types:

 

Traditional Techniques:

• Mean: We all know that the basic principle of mean is the ratio of the sum of observations by the number of observations.
• Mode: the item that has the majority of repeats is the mode. 
• Weighted Mean: It is nothing but the mean having some weights for their individuals.

Advanced Techniques:

• Boosting: Boosting is a type of ensemble learning that combines the weak learners and forms a strong learner to perform the predictions on new data.
  
• Bagging: Bagging is a combination of two words [bootstrap and aggregate]. Bootstrap means randomly creating samples of data out of a dataset with replacement, and Aggregation subsets of bootstrap data are given as training data to predict the aggregate output to new data when sampling is preferred. Replacement is called bagging, and without replacement, it is called posting.
  
• Random Forest:This algorithm builds multiple decision trees and merges them together to get more accurate and stable predictions. Then it gets predictions from each tree, and by means of majority voting, it selects the decision that gets the majority vote.
  
• Voting Classifier: A very simple way to create a better classifier is to aggregate the predictions of each classifier and predict the class that gets the most votes.
  
• Stacking [blending] was introduced by Walpert. It is also known as a stacked generalization, and it is an extended form of the model averaging ensemble technique in which all submodels equally participate as per the performance weights and build a new model with better predictions.
  

What are you going to see in the material?

• Classification and its types
• Performance measures
• MNIST dataset and different types of dataset
• about ensemble learning
• ensemble classification
• traditional ensemble learning
• advanced ensemble learning
• boosting
• bagging
• random forest
• voting classifier
• stacking 

link for the material: UNIT-3-ML

 

UNIT - 2 [PYTHON]

  UNIT-2 

We are committed to providing thorough Python resources that are especially suited to the JNTUK University R2O program. We will explore control statements in this blog post, going over topics like text output, loop formatting, and definite iteration. We will also look at conditional iteration with the while loop and selection statements, such as if and if-else statements.
Any computer language must include strings and text files, and Python is no different. We will go over data encryption methods and how to access characters and substrings within strings. In addition, we will discuss string and number systems and offer important insights into how they work. Finally, we will investigate the wide variety of string operations that Python offers and look at the nuances involved with handling text files.
Our goal at LetsLearningCse is to make learning Python entertaining and educational. We take great care in selecting our materials so that they correspond with the JNTUK University R2O syllabus and give students a thorough comprehension of the material. As we continue to assist you in your educational path, stay tuned for more interesting articles about Python.empowering students, one Python idea at a time using LetsLearningCse.

What are we going to gain from this material?

• Control statements: control statements are the program statements that allow the computer to select a part of the code or repeat an action to specify it. They are categorized into selection statements, iterative statements, looping statements, and jump statements.
  
• iterative statements: this was classified into mainly 2 categories.There are definite iterations and indefinite iterations.
• Definite Iteration: for a loop, it is a definite iteration; it iterates until the sequence exists.
•  conditional iteration: the A loop that requires a condition to be tested within the loop to determine whether the loop should continue is called the loop's continuation condition. If the continuation condition is false, the loop ends. If the continuation condition is true, the statements within the loop are executed again.
• While loop: it is a conditional iteration statement and is frequently used to execute a block of statements repeatedly until some condition remains true. 
  
• Formatting text for output: the total number of data characters and additional spaces for a given data in a formatted string is called its field width. The formatting output, rather than simply printing space-separated values, can be done in three ways: Formatting output using the string modulo operator (%) formatting output using the format method f-literal in Python
• strings: a string is a sequence of characters enclosed in single or double quotation marks. It is a data structure. A data structure is a compound unit that consists of several smaller pieces of data. The string is an immutable data structure.
• Data Encryption: Data encryption is used to protect information transmitted on networks. Data encryption is a security method where information is encoded and can only be accessed or decrypted by a user with the correct encryption key
•  Number Systems: When you perform arithmetic operations, you use the decimal number system. A binary number system is used to represent all information on a digital computer.
• Text files: all files must be opened first before they can be read from or written to using Python's built-in open() function. write(): This method is used to write the contents of a string to the file. read(): This method is used to read the contents of a file. remove(): to delete a file
  

 Link to the material:    UNIT-2
 

UNIT -1 [PYTHON]

 UNIT-1 

Greetings from LetsLearningCse.blogspot.com. This blog article will help you learn more about Python.

Note: This material is provided based on the JNTUK R20 syllabus.

Let's dive into the content you are all going to see in this material. In this, you can see what Python is and what a program development cycle is, and along with that, you are going to see what the datatypes, structures, precendence, etc.

Python is a high-level language that is flexible enough to deal with vast areas of work such as data science, machine learning, robotics, and AI assistant creation.

Why do most people prefer the Python language?

People prefer the Python language mostly for several reasons:

• simple and easy to learn.
• flexibility
• free and open source
• vast standard libraries
• scalability
• less SLOC [source lines of code]
• dynamically typed and interpreted language
• efficient data visualization after R-language
• Object-oriented programming and integrated
• GUI support
• cross-platform language/portability

Python was invented by Guido Van Rossum in 1991. In fact, so many people don't know why Python is named Python. There is a fact: when Guido van Rossum reads a famous BBC comic about Monty Python flying circus, he wanted to choose a name a little bit mysterious, so he chose Python as the name of his invention.

You can download Python from the www.python.org site. There are a vast number of platforms for Python, such as Idle 3.12, Notepad, Notepad++, Spyder, Jupyter Notebook, Pycharm, Sublime Editor, Anaconda, Visual Studio, etc.

Why should we learn Python? Python can help in various fields such as web development, machine learning, data analysis, scripting, game development, embedded applications for IOT, data science, deep learning, and natural language processing.

What do you gain from this material about Python?

• Input: how we provide the input in Python, such as static input and dynamic input.
• Processing: how is the input data processed in Python, and what are the stages involved in the processing of Python code?
• Output: how the output is displayed in the shell of a Python script
• Output with the print Function: The print function is an output function that returns the output data in the Python shell.
• Comments: Comments are necessary to learn how to provide a description of how the code works. In Python, we have single-line comments and multiline comments, also known as documentation.
• Variables: These are the containers that store data using a specific name. Here you'll see what the rules are for declaring a perfect variable and how we can manipulate a variable.
• Reading input from the keyboard: input() is a function to read the input from the user side into the program.
• Performing Calculations: How You Can Do Calculations Using Python will be seen.
• operators: what are the operators to perform the various calculations using Python, such as arithemetic, logical, and shift operations?
• conditional statements: they are used in decision-making using Python and are completed based on boolean values.
• if condition: if the given condition is true, then only code in its block is executed.
• if-else condition: the given condition is true. if block is executed; otherwise, block is executed.
• Repetition structures: in this, it shows how code can be repeated multiple times.
• While loop: until the condition is false, the loop is executed.
• for loop: until the sequence exists, statements are executed.

 link to the material: UNIT-1