QxQ: Introduction to Quantum Computing

• Oct 2023 - Present

QCourse 551-1

• Sep 2023 - Present

QIntern 2023

• Jul 2023 - Present

QNickel

• Oct 2023 - Oct 2023

Participated in QNickel 2023 workshop organized by QPoland. This workshop spanned four days - 21st, 22nd and 28th, 29th October respectively. The first two days covered Conventional Quantum Algorithms in Qiskit and the last two days covered Solving Max-Cut Problem using Grover Search in Cirq.

Qiskit Fall Fest-Ghana

• Oct 2023 - Nov 2023

It was a five days workshop spanned from 30th October till 3rd November. It was amazing an experience to solve interesting problems Quantum Computing each day. It was a five days workshop spanned from 30th October till 3rd November. It was amazing an experience to solve interesting problems Quantum Computing each day.

Project Intern

• Sep 2023 - Nov 2023

The goal of the project was to explore the practical application of quantum principles to solve real-world problems, through the development and implementation of various code modifications. The goal of the project was to explore the practical application of quantum principles to solve real-world problems, through the development and implementation of various code modifications.

Classiq Quest Bootcamp

• Oct 2023 - Oct 2023

Data Engineer

• Aug 2023 - Aug 2023

Project Intern

• Jun 2023 - Jul 2023

Worked on Tweet Sentiment Analysis using Bidirectional-LSTM. Worked on Tweet Sentiment Analysis using Bidirectional-LSTM.

Skill Development Trainee

• Feb 2023 - Apr 2023

The Crop Prediction System is an essential tool for farmers to make informed decisions about crop selection and yield prediction. In this project, we develop a Crop Prediction System using Python and Machine Learning techniques. The system incorporates key libraries such as NumPy, Matplotlib, Pandas, Seaborn and Scikit-Learn for data manipulation, visualization, and modeling. The user interface of the system is interactive using the "Interact" library, which allows for the selection of various… Show more The Crop Prediction System is an essential tool for farmers to make informed decisions about crop selection and yield prediction. In this project, we develop a Crop Prediction System using Python and Machine Learning techniques. The system incorporates key libraries such as NumPy, Matplotlib, Pandas, Seaborn and Scikit-Learn for data manipulation, visualization, and modeling. The user interface of the system is interactive using the "Interact" library, which allows for the selection of various input parameters to obtain crop yield predictions. Additionally, the "warnings" library is used to handle error messages in the system. To improve the accuracy of the model, we apply data normalization techniques using the "Normalizer" function. Finally, the "i-loc" feature is implemented to ensure the security of the system. The results of this project demonstrate the potential of using Python and Machine Learning for developing accurate and user-friendly Crop Prediction Systems. Show less The Crop Prediction System is an essential tool for farmers to make informed decisions about crop selection and yield prediction. In this project, we develop a Crop Prediction System using Python and Machine Learning techniques. The system incorporates key libraries such as NumPy, Matplotlib, Pandas, Seaborn and Scikit-Learn for data manipulation, visualization, and modeling. The user interface of the system is interactive using the "Interact" library, which allows for the selection of various… Show more The Crop Prediction System is an essential tool for farmers to make informed decisions about crop selection and yield prediction. In this project, we develop a Crop Prediction System using Python and Machine Learning techniques. The system incorporates key libraries such as NumPy, Matplotlib, Pandas, Seaborn and Scikit-Learn for data manipulation, visualization, and modeling. The user interface of the system is interactive using the "Interact" library, which allows for the selection of various input parameters to obtain crop yield predictions. Additionally, the "warnings" library is used to handle error messages in the system. To improve the accuracy of the model, we apply data normalization techniques using the "Normalizer" function. Finally, the "i-loc" feature is implemented to ensure the security of the system. The results of this project demonstrate the potential of using Python and Machine Learning for developing accurate and user-friendly Crop Prediction Systems. Show less