o Machine Learning algorithms
o Statistics and linear algebra
o Data structures and basic algorithms
- Working knowledge of Python
- Familiarity with ML libraries such as:
o scikit learn
o TensorFlow or PyTorch (basic exposure is sufficient)
- Basic understanding of SQL and working with datasets
Good to Have (Not Mandatory)
o Cloud platforms (Azure / AWS / GCP)
o Data platforms like Snowflake
o ML lifecycle concepts (training, evaluation, deployment)
- Academic or personal projects involving:
o Predictive modeling
o NLP or computer vision
o Time series forecasting
- Familiarity with notebooks, Git, or basic MLOps concepts
What You Will Learn
- End to end AI/ML use case development in an enterprise environment
- Working with real production scale datasets
- Model experimentation, evaluation, and promotion practices
- AI/ML platform tools and best practices
- How ML solutions are governed, monitored, and scaled
