Vidhathri\'s report
13 / 4 / 2023
Name:Vidhathri
Domain:AIML
Branch: CSE 2nd year
TASK-1 :- API
API stands for Application Programming Interface .
In this task I learnt about API and to with it. I got to know about rapid API, where we can find ,test and connect to thousands of API's by using API key. Firstly I created an account in OpenWeatherMap and got API key. Then I created a website using HTM S SON. After making website I put API key in the code authorisation and fetched details from OpenWeather API. Then we can get details about temperature, humidity, windspeed of different places by entering the place name .
Now a days API's are most commonly used in Twitter bots, Log-In using XYZ, Weather snippers, Pay with payPal, Google maps, Travel booking, E-Commerce.
\https://github.com/vidhathri30/MTask2.git\"
](https://user-images.githubusercontent.com/101579638/233794583-685c0cd0-8a55-4532-a041-da03ac1e5119.jpeg
TASK-4 :- Portfolio Webpage
As per the task I created a portfolio webpage which contained about myself, interests, projects, social media links. For this project I learnt HTML and basics of CSS from Udemy platform. I did website using various basics tags and few concepts of CSS. After completion of the website I created a new repository in a github and pushed the portfolio folder to it. Then I went to settings of this repository and deployed it from branch to get webpage link.
The github repository-"https://github.com/vidhathri30/Marvel--Portfolio.git\"
The webpage link-"https://vidhathri30.github.io/marvel\"
TASK-5 :-Working with Github
In this task firstly I forked the repository given by Marvel to my github account . Then I cloned the repository to the directory needed by using git clone command in gitbash. Once done with cloning I created a new branch by using git branch command in gitbash. Then I corrected the error in the the main.py file and commited the changes using commit -m command in gitbash. Later pushed the changes by using git push command. Then at last I created pull request.
In this task I learnt about Git and how to use it by using commands. I Learnt to create repository , pull request and many more.
"https://github.com/vidhathri30/git-task.git\"
TASK-6 :-Tinkercad
Firstly I created a tinkercad account . I took too much time to understand this application. I looked thoroughly on the circuit given in a example and tried to implement it to estimate the distance between an obstacle and the sensor using Ultrasonic sensor and displayed the reults on LCD screen . I used Arduino Uno R3 board , Ultrasonic sensor (HC-SR04), 16×2 LCD I2C Display and Jumper Wires .
Applications of Ultrasonic Distance Measurement: Used in RADAR system, to measure distance without physical contact with measuring instruments, used in object detection for security purposes.
The code and the images I used to run and implement this circuit-
](https://user-images.githubusercontent.com/101579638/235215956-0b449243-5421-4372-9e52-cf791232f1c7.jpeg
TASK-8 :- : LED Toggle Using ESP32
In this task we created a standalone web server with an ESP32 to controls the LED connected with ESP32 GPIOs or output using the Arduino IDE. We made a circuit as shown in reference using ESP32 development board , 2x 5mm LED, 2x 330 Ohm Resistor, Breadboard, Jumper wires. Then connected ESP32 board to PC via micro USB cable. Next selected ESP32 board and COM port in Arduino IDE . Used the code given in reference and pasted it in Arduino IDE to implement it. Uploaded the code to ESP32 by clicking upload button in IDE.
 and other electronic components. It is also used in plumbing and metalwork, as well as in the manufacture of jewelry and other decorative items.
Some images of this task-
TASK-10 :-Active Participation
I actively participated in many technical event conducted in our college. I also volunteered for a Impetus fest and organized a event in Inspiron fest. During this I learnt many skills, improved my communication ability, got to involve with many people.
Certificates :-
](https://user-images.githubusercontent.com/101579638/231870340-972ecc51-a9fc-492b-8564-e553814c9a86.jpeg
I enrolled for a Udemy course and completed it . In this course I learnt about HTM SS in 4hours.
Certificate: "https://www.udemy.com/certificate/UC-1dec412e-2ea3-4423-83b2-9e186ac6a5af/\"
TASK 1-Linear and Logistic Regression - HelloWorld for AI-ML
Linear Regression is a machine learning algorithm that determines the relationship between one dependent variable (y) and one or more independent variables (x). It models a target prediction value based on the dependent variable, using this determined relationship.
A linear regression line has the equation Y=mX+b
Logistic Regression:-Logistic Regression is a classification algorithm. It models the probability of an event taking place by plotting the logarithmic sigmoid (to bring the output between 0 and 1) of a linear combination of independent variables.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task1.md
TASK 2-Matplotlib and visualizing data
Data Visualization is the process of presenting data in the form of graphs or charts. It helps to understand large and complex amounts of data very easily. It allows the decision-makers to make decisions very efficiently and also allows them in identifying new trends and patterns very easily.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task2A.md
TASK 3-Metrics and Performance Evaluation
- Regression Metrics - used to evaluate performance of regression algorithms
The regression model predicts continuous target values. These are different from the classification metrics because they should be able to work on continuous values.
- Classification Metrics - used to evaluate performance of classification algorithms
Classification is one of the widely used models in Machine Learning. It mainly involves predicting classes depending on the input we have. Binary classification is the type of classification where only two classes are involved.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task3.md
Task 4: Linear and Logistic Regression
linear regression is a machine learning algorithm that determines the relationship between independent and dependent variables. It models a target prediction value determined from this relationship. There are two main concepts involved in implementing this - cost function, gradient descend.
logistic regression : works using the sigmoid function to return a probability curve using the sigmoid function. This logistic function returns a probability value which then leads to either one of two discrete classes. The sigmoid function keeps all values between 0 and 1 and is given by the equation.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task4.md
TASK 5 K- Nearest Neighbor Algorithm
K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique. K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task5.md
TASK 6- : An elementary step towards understanding Neural Networks
Blog on Neural Networks and types like CNN, AN n https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task6A.md
Building GPT 4
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/TASK6B.md
TASK 7-: Mathematics behind machine learning
Curve fitting-Curve fitting is an optimization problem that finds a line that best fits a collection of observations.
It is easiest to think about curve fitting in two dimensions, such as a graph.
Fourier Transforms-Fourier Transform is a mathematical technique that helps to transform Time Domain function x(t) to Frequency Domain function X(ω).
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efc
TASK 8-: Data Visualization for Exploratory Data Analysis
Data visualization is an important component of Exploratory Data Analysis (EDA) because it allows a data analyst to “look at” their data and get to know the variables and relationships between them. It gives an idea about the data we will be digging deep into while analyzing.
https://github.com/vidhathri30/L1Report/blob/2952bd8fa354f4a2f07b39420eadce257a45a4d0/Task8.md
TASK 9 - Decision tree.
A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. The decisions or the test are performed on the basis of features of the given dataset.
https://github.com/vidhathri30/L1Report/blob/256e5374c4a9122af28290efcb43ebdea230af86/Task9.md
TASK 10 -: Exploration of a Real world application of machine learning
https://github.com/vidhathri30/L1Report/blob/f7016dc8b59849a5e5ce4244136e55e527296431/Task10.md
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