R20-2-1-OOPS through C++

R20-2-1-OOPS through C++ UNIT -WISE MATERIALS:-

UNIT-1              UNIT-2       UNIT-3

R20-2-1-OOPS through C++  LAB MANUAL:-

Lab Manual

 Welcome to the letslearningcse , in this blog post you are going to learn c++ based on JNTUK R20 syllabus

Title: Unit-specific C++ Resources Prepared Using the JNTUK R20 Syllabus

Overview : The programming language C++ is strong and adaptable, and it's widely used in software development. Adhering to a thorough and well-organized curriculum is crucial for guaranteeing a strong comprehension of the language. We will talk about the first three units of the C++ unit-wise resources in this blog post. These materials are based on the JNTUK R20 syllabus and offer a comprehensive and well-rounded learning experience.

 Unit 1: Overview of C++ : Students are introduced to the fundamentals of C++ programming in the first unit of the syllabus. Data types, variables, operators, and control statements are among the subjects discussed. The structure and syntax of C++ programs are taught to students, with a focus on the significance of good coding practices. With practical tasks, students get hands-on experience building basic C++ programs, which lays the groundwork for more complex subjects in later units. Students learn about the basic ideas of C++ programming in this lesson, including data types, variables, and operators. They gain knowledge of how to employ control statements like if-else and switch-case, declare and initialize variables, and carry out arithmetic and logical operations. Students will be able to write basic programs and have a firm grasp of the fundamentals of C++ programming by the end of this lesson.

Unit 2: Loops and Control Statements Control statements and loops, which are essential elements of any programming language, are covered in detail in the second unit. Students investigate the complexities of using if-else statements to make decisions, loops like for and while to iterate, and the manipulation of lists and other data structures. Comprehending these ideas is essential for crafting effective and rational code, empowering learners to approach challenging issues methodically. Students study the many kinds of C++ loops and control statements in this unit. They look at iteration statements like for, while, and do-while as well as decision-making statements like if-else and switch-case. Along with learning how to work with data structures like arrays and lists, students also learn how to use the break and continue statements in loops. Students will be able to use control statements and loops to construct sophisticated programs by the conclusion of this lesson. 

Unit 3: Object-Oriented Programming and Functions The third course explores the concept of object-oriented programming, or OOP, which is utilized extensively in contemporary software development. Pupils gain knowledge about the role functions play in improving reusability and modularizing programming. They also explore OOP concepts like polymorphism, inheritance, classes, and objects, which help create scalable and reliable programs. Students study the use of functions in C++ programming in this subject. They study the many kinds of functions, including user-defined and built-in functions, and they discover how to provide parameters to functions and get values back. The fundamentals of object-oriented programming, including classes, objects, inheritance, and polymorphism, are also taught to students. By the end of this course, students will understand how to use functions and the concepts of object-oriented programming to write modular, reusable code. 

In summary The JNTUK R20 syllabus served as the basis for the preparation of the first three units of the unit-wise C++ resources an organized method for picking up the fundamentals of this potent programming language. As they work through these lessons, students will gain an understanding of the fundamentals of C++ programming as well as the problem-solving abilities necessary for a successful software development career.

 The JNTUK R20 syllabus offers a thorough and well-rounded education, encompassing every facet of C++ programming, from fundamentals to more complex subjects. Students can be confident that by adhering to this syllabus, they will acquire a strong grasp of the language and be well-equipped to start a lucrative career in software development.

R20-AI&ML3-1 ----------Machine Learning [ML] Notes

 material links of unit 1&2&3:

UNIT-1      UNIT- 2&3

Lab manual link:

Machine Learning-LAB-R20-3-1

LetsLearningCse.blogspot.com welcomes you to the world of knowledge on technology. In this blog post, you are going to experience a new kind of knowledge regarding artificial intelligence and machine learning. Without delay, let's get into the topic.

Well, let's start with a brief introduction to artificial intelligence and machine learning.

Artificial intelligence is a branch of computer science in which we can create intelligent machines that can behave like humans, think like humans, and make decisions. Actually In 1951, the first AI program was introduced by Christopher, and AI was introduced by John McCarthy in 1955.

 

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.It was introduced by Arthur Samuel in 1959. It enhances systems to learn from data, improve performance from experience, and predict new data without human interference. Machine learning is a subset of AI.

Deep learning is a collection of statistical techniques for machine learning feature hierarchies that are actually based on artificial neural networks. I was introduced by Dina Dechter in 1986. It is subset of Machine learning

Before going into our main concept of machine learning, see various areas that use machine learning; they are image recognition, virtual personal assistants, automatic translation, traffic prediction, web search and recommendation engines, online fraud detection, medical diagnosis, text and speech recognition, email spam filtering, etc.

Tip: If you know this 7-step process in machine learning, it's clear that you can build any project in machine learning in an efficient manner.

• Define the problem. [Understand the problem first.]
• Data Gathering
• Data preprocessing [noisy data removal, data splitting]
• Choose model
• Train the model.
• Test the model.
• Deployment or prediction

Types of machine learning:

• Whether or not they are trained under human supervision (SUPERVISED, UNSUPERVISED, SEMI-SUPERVISED, and REINFORCEMENT LEARNING).
• Whether or not they can learn incrementally on the fly (Online Learning vs Batch Learning)
• Whether they work by simply comparing new data points to known data points or instead detect patterns in the training data and build a predictive model [instance-based learning versus model-based learning]

Most of the time, we discuss the human supervision type of machine learning.

• Supervised learning
• Unsupervised learning
• semi-supervised learning
• Reinforcement learning

Supervised learning: we train the machines using the labeled dataset, and based on the training, the machine predicts the output. In this, we feed training data to an algorithm that includes the desired solutions in it. It has two methods to solve problems, which are:

• Classification: it was used when output data is categorical data.
• Regression: it was used for predicting or estimating a continuous or quantitative output value.

e.g., image recognition, spam detection

Unsupervised learning: In this model, the model, the models are trained using an unlabeled dataset and predict new data without any supervision, so we say it is unsupervised. It was easier as compared to a labeled dataset. It has two methods:

• Clustering: method of grouping data items that are similar into a single cluster
• Association: It is used for finding the relationships between variables in large data sets.

Semi-supervised learning lies in between supervised and unsupervised learning. To overcome the drawbacks of both, it came into existence. Its training data is a combination of both labeled and unlabeled data. It has both the features of supervised and unsupervised learning.

Reinforcement learning: we simply say it as feed-back learning. It follows the trail-and-error method to get the desired solution.

Loss Functions:

Loss is a number indicating how bad the model's prediction was on the sample

Training loss: it is the error of the model on the training set.

Testing loss: it is the error of the model on the testing set.

A loss function measures the error between the predicted and actual values in a machine learning model.

There are different loss functions, such as

• Mean Squared Error (MSE)
• Cross-entropy loss
• Mean Absolute Error (MAE)
• Huber loss
• Hinge loss
• Quantile loss