As a Data analysts work on projects that involve collecting, processing, and analyzing data to extract meaningful insights. Some common projects include building dashboards and reports, conducting exploratory data analysis, developing data visualization tools, and creating predictive models using techniques like regression and clustering.
As an AI engineers work on projects that involve developing and deploying artificial intelligence systems. Some common projects include building machine learning models for tasks like image recognition, natural language processing, recommendation systems, or predictive maintenance. They may work on projects related to computer vision, speech recognition, robotics, or autonomous systems. AI engineers also work on integrating AI models into applications, optimizing model performance, and ensuring the responsible development of AI systems.
As a Web developers work on projects that involve building and maintaining websites and web applications. Some common projects include developing e-commerce platforms, creating content management systems, building social media applications, or developing web-based tools and services.
Projects
HR Department Analysis
HR Department Analysis in Power BI involves creating comprehensive visualizations and insights derived from HR data. It encompasses metrics like employee turnover rates, performance evaluations, and recruitment effectiveness. Utilizing Power BI's tools, it enables HR professionals to track, analyze, and optimize various aspects of workforce management for strategic decision-making.
AI-object-detection-master
An AI Object Detector Master is a system trained to accurately identify and locate objects within images or videos using artificial intelligence techniques. It employs advanced algorithms to detect objects of interest, enabling applications such as surveillance, autonomous vehicles, and image recognition systems to interpret and interact with their surroundings effectively the process of training the whole batch again.
Visualizations using various libraries
Visualization libraries in Python enable users to create intuitive and interactive data visualizations that can effectively communicate insights to a broad audience. Some of the popular visualization libraries and frameworks in Python include Matplotlib, Plotly, Bokeh, and Seaborn. These libraries offer a range of features, from basic plotting to advanced interactive visualizations, and cater to different levels of complexity and use cases. The choice of library often depends on the specific requirements of the project, the type of data being visualized, and the desired level of interactivity and customization.
AI Face Recognition
AI face recognition projects in Python often rely on popular libraries and modules such as OpenCV, Dlib, and face_recognition. OpenCV (Open Source Computer Vision Library) provides tools for image and video processing, including face detection algorithms. Dlib is a C++ library with a Python interface, offering facial landmark detection and face encoding capabilities. The face_recognition module, built on top of Dlib, simplifies the process of recognizing faces in images or video streams. These libraries leverage machine learning techniques, such as Histograms of Oriented Gradients (HOG) and deep learning models, to accurately detect and identify facial features. Additionally, libraries like NumPy and scikit-learn may be used for data manipulation and machine learning tasks involved in face recognition projects.
Website - Aroma Hotel
This website has been created as a coursera project for end of course assessment for the course Advance HTML and Css by Meta. The website is created for a fictitious restaurant known as Aroma Hotel. The website is responsive and scales over all screen sizes.
Form validation using JS
This website has been created as a part of learning Javascript.
Ain't Much, But honest work :)