This is my first research assignment on machine learning using teachable machines (project). Its not a big deal, I know, but I'm inspired by this as an IT executive.

1. Assignment
Assignment Title : Exploring creative coding and Machine Learning
Project Name : Machine Learning using Teachable Machine
Name : Tanu Malakar
Position : ICT Content Creator (Intern)

2. Machine Learning using Teachable Machines: A teachable machine is a web-based
application that creates models easily and quickly. There are three purposes: image, audio, and
pose models. The good part is that it is flexible. It can teach a model to classify images or poses
through images or a live webcam. It is free and best for students. The model that is created
through Teachable Machine is a real Tensorflow model that one can integrate with a web app, an
Android app, or any platform. Neither requires the creation of an account. It made everything so
simple.
Methodology:
1. Inspire:
1. Understand the basics: Familiarize yourself with the concepts and techniques used
in image, audio, and pose modeling. Learn about neural networks, convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and other relevant
algorithms.
2. Showcase real-world applications: Start by presenting examples of image, audio, and pose
models being used in various industries. Highlight how these models have revolutionized
fields like healthcare, entertainment, sports, and security. This will help students see the
practical applications of these models and get excited about their potential.
3. Explore existing models: Look for pre-trained models and projects that have been
successful in image, audio, and pose recognition. Study their architectures, datasets,
and performance metrics to gain insight into their working.
4. Stay updated: Image, audio, and pose models are evolving rapidly. Stay up-to-date
with the latest research, techniques, and tools in the field. Follow relevant research
papers, attend conferences, and subscribe to newsletters or blogs to keep yourself
informed.
2. Engage:
1. Collaborative activities: Foster collaboration among students by organizing group
activities or projects that involve image, audio, and pose models. Assign tasks that require
teamwork, such as creating a multimedia presentation, developing a game, or solving a
problem using these models. This collaborative approach will encourage peer learning and
engagement.
2. Participate in competitions and challenges: Engage in data science competitions or
challenges that involve image, audio, and pose modeling. Platforms like Kaggle often
host competitions that require participants to develop innovative models for various
tasks. This can help you test your skills, learn from others, and gain exposure to real-
world problems.
3. Multimedia resources: Utilize multimedia resources such as videos, animations, or
interactive tutorials to explain the concepts and functionalities of image, audio, and
pose models. Use visual and auditory aids to make the learning experience more
engaging and appealing to different learning styles.
3. Practice:
1. Set up a development environment: Set up a development environment on your
computer or use cloud-based platforms like Google Colab or Kaggle Kernels. Install

3. the necessary libraries and frameworks, such as TensorFlow, Keras, PyTorch, or
OpenCV, depending on your chosen model and programming language.
2. Start with pre-trained models: Begin your practice by using pre-trained models.
These models are already trained on large datasets and can be used as a starting point
for your own applications. TensorFlow Hub and PyTorch Hub provide a collection of
pre-trained models that you can explore and use.
3. Document and share your work: Maintain a detailed record of your experiments,
methodologies, and results. Documenting your work will not only help you track your
progress but also enable you to share your knowledge with others through blog posts,
tutorials, or GitHub repositories.
4. Continuous learning and improvement: Machine Learning is a rapidly evolving
field, so it's crucial to remain curious and open to learning. Attend workshops,
webinars, and conferences to expand your knowledge and network with experts in the
field.
4. Master:
1. Understand the Basics: Before diving into mastering these models, ensure you have a
solid understanding of fundamental machine learning concepts, including data
preprocessing, model architecture, training, validation, and testing.
2. Study the Specific Domain: For each model type, delve into domain-specific
knowledge:
Image Models: Learn about image preprocessing, augmentation, transfer learning,
and techniques to enhance model performance.
Audio Models: Understand audio signal processing, spectrograms, and methods like
Mel-frequency cepstral coefficients (MFCCs) for audio feature extraction.
Pose Models: Study key point detection, pose estimation algorithms, and how to
work with joint coordinates.
3. Stay Updated with Research: The field of image, audio, and pose modeling is
constantly evolving. Stay updated with the latest research papers, attend conferences,
and follow experts in the field. This will help you stay ahead of the curve and
incorporate state-of-the-art techniques into your work.
4. Build a Portfolio: Document your projects, methodologies, and learnings in a
portfolio. Having a showcase of your work can be valuable when seeking job
opportunities or collaborations.
5. Learn from Feedback: Seek feedback from peers, mentors, or online communities.
Constructive criticism can help you identify areas for improvement.