AI Data Analyst Faculty
1 week ago
- Conduct sessions on AI & ML Fundamentals, Model Building & Evaluation, and Data Visualization & Communication.
Deliver lectures on:
Introduction to AI & ML, Core ML Techniques, and Real-world Applications.
- Supervised, Unsupervised, and Reinforcement Learning.
- Neural Networks, Deep Learning, and Transfer Learning.
- Data Preprocessing, Model Evaluation, Overfitting/Underfitting, and Responsible AI.
Data Visualization principles, Storytelling, and Capstone Projects.
Provide guidance on hands-on assignments and case studies.
- Assess student performance and share feedback for continuous improvement.
- Ensure all sessions align with course objectives and current industry practices.
Tools and Technologies:
The faculty should be proficient in the following:
- Python
- Jupyter Notebooks
- TensorFlow, Keras
- OpenCV, Scikit-learn, Seaborn, Pandas, Matplotlib
- Power BI (Basics)
Qualifications & Experience:
- Bachelors or Master's degree in Computer Science, Data Science, AI/ML, or a related field.
- Proven teaching or training experience in AI, ML, or Data Analytics.
- Hands-on experience with the tools mentioned above.
- Strong communication and mentoring skills.
- Industry experience in AI/Data projects will be an added advantage.
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