COURSE
4 Levels · 6 Months
An introductory course on Artificial Intelligence and Machine Learning.
Understand the working of a 3D printer, check out the online resources. Understand what's an STL file, and then learn to slice it (using ultimaker or creality slicer).Go through the SOP'S regarding the 3d printer. Learn about bed temperature, infill density and other printer settings. Finally get an STL file from the internet, and slice it and put it for print.
Resources:
(Note this task is to be done under coordinator supervision.)
What is an API? Learn the working of an API and its applications. Using any api of your choice, build an user interface(web app, mobile app, etc), where you can make calls and then display the necessary information. An example weather app is given below, using the open weather api.
● Example
Familiarize yourself with GitHub integrated workflows (GitHub actions), Issues, and pull requests with this task. Given below is a git repository, go check it out and then perform the necessary tasks stated in the readme file.
Check this link for more info: https://github.com/UVCE-Marvel/git-task
● Create a folder named test.
● cd into that folder.
● Create a blank file without using any text editor.
● list the files in that folder
● create 2600 folders in this folder where each folder is named like . For example, M90 or B56.
● concatenate two text files containing any random text and display them on the terminal.
https://ubuntu.com/tutorials/command-line-for-beginners#1-overview
Make a kaggle account, visit the website and complete the competition
Participate in the Titanic ML competition – the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works.The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
Resources: Titanic regression model - https://www.kaggle.com/c/titanic
Video reference - https://youtu.be/I3FBJdiExcg
Using pandas and matplotlib, and a dataset of your choice, plot a line graph, bar graph, and scatter plot.
Reference: https://realpython.com/pandas-plot-python/
Create a website to showcase your portfolio - about yourself, interests, projects, social media profiles and more. It has to be responsive and also pushed to the git repository. CSS can be of your choice and any framework can be used.
Markdown is an easy-to-use markup language that is used with plain text to add formatting elements (headings, bulleted lists, URLs) to plain text without the use of a formal text editor or the use of HTML tags. Markdown is device agnostic and displays the writing format consistently across device type. Write a technical resource article on a topic of your choice and post it on the MARVEL website. Refer to the linked article for further details
Create a tinkercad account, get familiar with the application, understand the example circuits given and simulate a simple circuit using an ultrasonic sensor to estimate the distance between an obstacle and the sensor. Display the results on the serial monitor.
Create a radar system utilising an ultrasonic sensor and servo motor to detect objects within a certain range. The ultrasonic sensor emits sound waves and measures the time taken for them to bounce back, while the servo motor rotates the sensor to cover a wider area, providing a simple yet effective detection mechanism. RESOURCE: https://youtu.be/NwmcNCvUcDc?si=x2LAYMFiqs1SzLfI TASK OUTCOME: introduction to- · TINKERCAD · Working of ultrasonic sensor and servo motor · Radar technology PRECAUTIONS/SAFETY MEASURES- NOT ANY
Explore basic techniques for controlling DC motors, understand the control DC motors using the L298N motor driver and the Arduino board. Using an UNO and H-Bridge L298N motor driver, control the speed of a 5V BO motor, try simulating this on tinkercad and then perform it on the hardware, Record videos of you doing the same.
Learn the working of an ESP32 and create a standalone web server with an ESP32 that controls the LED connected with ESP32 GPIOs. Use the arduino IDE to code and upload the program to the ESP32. Learn to configure the IDE to upload code to an ESP32.
(Soldering is to be done in presence of a coordinator)
Learn about the soldering equipment present in our lab, the solder, the soldering iron, soldering wick, flux, etc. Learn to use them and perform basic soldering on a perf board, for example a LED circuit in the presence of a coordinator and document the same.
Design a 555 astable multivibrator with duty cycle 60%, rig up the circuit on a breadboard and by using the probes observe the output of your circuit on the DSO. Resources:
● Circuit
Description: For 4 cases, based on door lock/open and key pressed/not pressed. Determine the karnaugh map and make a burglar alarm using simple logic circuits. The buzzer or led blinks when certain conditions are met, you can use push buttons for the door and key.
(Tip: use logic gates, use k-maps to figure out the working conditions.)
Take part in any technical event, inter or intra college and submit the issued certificate of participation.
Enroll for a MOOC and complete the course.
Topics: 1)MQ135 Gas sensor 2)L293D motor driver Task Description: Study the datasheet of any one of the above and write a report on it. Specify about the ICs used in L293D, PWM, H-bridge etc. In case of MQ 135, specify the calibrations for different gases and the Freundlich Absorption Theorem Graph.
Familiarise yourself with what Virtual Reality is. Make a detailed study about what's the difference between VR and AR. Mention about the trends in the space and technology stack being developed. Make about Indian companies in this space. Make the report with detail. Using generative AI to generate this study can lead to disqualification.
Sadservers is an excellent ground to test your Linux troubleshooting skills. Here is a troubleshooting scenario: Command Line Murders. Troubleshoot and Make Sad Servers Happy!
Command line murder
Linux commands
Linux commands
Using express create a resource library website where you can browse the resource articles, books etc which are available and also manage your account
Reference
linear_model.LinearRegression()
linear_model.LogisticRegression
Explore the various basic characteristics to plots as given below, with python libraries:
Explore the given plot types:
https://medium.com/mlpoint/matplotlib-for-machine-learning-6b5fcd4fbbc7
https://towardsdatascience.com/data-visualization-for-machine-learning-and-data-science-a45178970be7
https://www.mygreatlearning.com/blog/matplotlib-tutorial-for-data-visualisation/
https://medium.com/mlpoint/matplotlib-for-machine-learning-6b5fcd4fbbc7
[https://archive.ics.uci.edu/ml/datasets/Absenteeism+at+work] https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a
Using Numpy Generate an array by repeating a small array across each dimension and Generate an array with element indexes such that the array elements appear in ascending order.
Reference : [https://numpy.org/doc/]
neighbors.KNeighborsClassifier
for multiple suitable datasets
Deepen your understanding with proper mathematical constructs that act as backbones for the algorithms you write and implement. Perform the below tasks as indicated:
Curve-Fitting- Model a curve fitting for a simple function of your choice, on Desmos.
Fourier Transforms- Fourier transforms are perhaps the most important function approximators used today. Model a fourier transform for a function of your choice on MATLAB.
References
Curve Fitting
Fourier Transforms
Use Plotly for data visualization. This is an advanced visualization library, more dynamic than the generally used MatPlotlib or Seaborn.
Decision Tree is a supervised learning algorithm that can be used for Regressive or Classifying Tasks. It is a way to use conditional statements as a hierarchy so that, for an event, you get the chances of given outcomes.
Support vector machines are supervised learning methods to create a non- probabilistic linear model, where a data value is assigned one of two classes to maximise the difference between the two classes. The data points are vectors and a hyperplane between the two classes is selected optimally to maximize the difference and regularize the loss.
Resources:
Image classification is an important area in real world applications of machine learning. K means clustering is a simple algorithm that uses clusters or collections of data and finds ‘k’ number of centroids, by averaging it out, such that k is minimum. Resources:
Anomaly detection is a way to detect erroneous data points in a stream, by looking at statistical differences. Anomaly detection can be done through unsupervised or supervised learning methods.
Resources:
Develop a generative adversarial network (GAN) model to generate realistic images of a specific category, such as faces, animals, or landscapes. Customize the GAN architecture and train it on a dataset relevant to the chosen category to produce high-quality and diverse synthetic images.
Resources:
Utilize LangChain, a natural language processing framework, to extract relevant information from PDF documents based on user queries. Develop a system that can interpret user queries, process PDF documents, and retrieve relevant sections or excerpts using language understanding techniques.
Resources:
Task Outcomes:
Employ PaddleOCR, an Optical Character Recognition (OCR) toolkit, to extract and analyze tabular data from images or scanned documents. Develop a pipeline that can accurately detect tables, extract data, and perform analysis such as statistical computations or data visualization.
Resource Links:
Task Outcomes:
Here's an outline of the steps we'll follow: