#💫 About Me: 🔭 I’m currently working on Blockchain, AWS Bed rock, and Gen AI
👯 I’m looking to collaborate on AI, ML, and Blockchain
🤝 I’m looking for help with any Consulting.
👀 I’m interested in Blockchain, Metaverse, Image processing AI, ML, and crypto technologies.
🌱 I’m currently learning GO lang, ansible, and Metaverse.
💬 Ask me about anything that you need help
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Sage Maker 📝 I write Blogs 👉 [here](https://medium.com/@aadhi0612)

Now I have started to learn and going to write the exams as below:

AWS Certified Machine Learning

Let’s unite for a shared purpose to empower everyday people to change the world with Data!

Machine learning is a subfield of AI, which enables a computer system to learn from data. ML algorithms depend on data as they train on information delivered by data science. Without data science, machine learning algorithms won't work as they train on datasets. No data means no training.

In machine learning, one of the things that should be taken care of is the type of data given to the model. If we have more data, there is a higher chance for a machine learning algorithm to understand it and give accurate predictions to the unseen data respectively.

Data Science involves analysis, visualization, and prediction. It uses different statistical techniques, while AI and Machine Learning implements models to predict future events and makes use of algorithms.

With CloudnLoud Tech Community support we are Planning to bring many cloudnative real-time knowledge sharing schedules.

Social Handle- LinkedIn

• Unveiling the World of (VAEs)
• Are artificial intelligence and machine learning the same?
• Will artificial intelligence replace humans?
• Why artificial intelligence is important?
• Where AI is used?
• The Role of Python Libraries and How to Utilize Them Effectively
• Building a Face Mask Detector using Python and OpenCV
• What is Generative AI: How does it Revolutionize AI
• What is a Neural Network? How to Visualize It? #Blog-2
• Understanding AI: How It Works and Its Impact

Gave a speech and hands-on demo in 20+ meetups 10 in person and the other five virtual and another 5 in closed events.

Took a session in detail on how to use .ipynb files in aws and explained by S3 bucket and then accessing it through a Jupyter Notebook instance.

• Here is the repo - Real World Applications of AI and ML
• LinkedIn Post Images

• more than 150+ members attended this event.

Took a session in detail on how to use .ipynb files in aws and explained by S3 bucket and then accessing it through a Jupyter notebook instance.

• Here is the repo - Generative AI hands On Deep dive

• LinkedIn Post Gen AI Meetup - Meetup

• LinkedIn Post Gen AI Meetup - Meetup

• LinkedIn Feedbacks More than 30 members attended this event - Feedbacks

• LinkedIn Post Images

In this project, we explore the integration of Elastic Search with Generative AI techniques to enhance search capabilities and generate novel content. We've implemented three distinct use cases to showcase the potential of this integration.

We've utilized Generative AI models to transform voices. The accompanying .ipynb file contains the code used for this purpose. To run the notebook:

1. Make sure you have the required libraries installed (specified in the notebook).
2. Open the .ipynb file using a Jupyter Notebook environment.
3. Execute the cells step by step to generate voice transformations.

In our second use case, we demonstrate the ability to generate images directly from a Large Language Model (LLM). This can have various applications, such as content creation and artistic design. The .ipynb file associated with this use case contains the code.

To run the notebook:

1. Set up a compatible environment with the necessary libraries (outlined in the notebook).
2. Open the .ipynb file using a Jupyter Notebook platform.
3. Follow the provided instructions to generate images using the LLM model.

Our third use case involves the implementation of Drag GAN (Generative Adversarial Network). Drag GAN is a specialized model for generating images with a focus on specific attributes.

To explore this use case:

1. Access the .ipynb file associated with the Drag GAN use case.
2. Ensure your environment includes the required dependencies (as specified in the notebook).
3. Open the .ipynb file using a Jupyter Notebook environment.
4. Execute the cells sequentially to understand and experiment with Drag GAN.

I'm also a Certified Blockchain Developer by the Blockchain Council