Used for ML, data analysis, and automation.
Used for building and evaluating machine learning models efficiently.
Deep learning frameworks for neural networks, CNNs, RNNs, and model deployment.
Familier with ANNs,CNNs, RNNs, and training models.
Supervised, unsupervised learning and model deployment.
Pandas, NumPy, Matplotlib, Seaborn for exploring and visualizing data.
Plotly, Matplotlib, Seaborn to create interactive charts.
Text processing, sentiment analysis, and feature extraction.
Image classification, detection, and OpenCV/Yolo implementations.
Building responsive and structured web pages.
Styling pages and structuring.
Adding interactivity, small web apps.
Querying and managing relational databases.
Version control and collaborative project management.
Building web/ML/data science applications using Python.
good foundation in arrays, String, graphs, sorting, searching, and algorithm design.
Strong logical and analytical thinking for algorithmic and real-world challenges.
Ability to extract insights from data and make data-driven decisions.