2 - 7 years
0 - 2 Lacs
Posted:1 day ago|
Platform:
Work from Office
Full Time
Required Skills:
Core Machine Learning: Strong understanding of supervised, unsupervised, and reinforcement learning concepts with real-world applications.
Key ML Techniques: Hands-on experience with regression, classification, decision trees, ensemble methods (e.g., Random Forests, XGBoost), and support vector machines (SVMs).
Data Analysis & Clustering: Practical knowledge of dimensionality reduction (e.g., PCA) and clustering algorithms (e.g., K-Means, DBSCAN).
AWS Machine Learning Stack: Proficiency in using Amazon SageMaker and other AWS tools for building, deploying, and managing machine learning workflows.
Advanced Concepts: Familiarity with deep learning models, reinforcement learning strategies (e.g., Q-Learning, MDP), and model optimization techniques (e.g., hyperparameter tuning, Optuna).
ML Tools: Comfortable working with tools such as scikit-learn, TensorFlow/Keras, pandas, and related Python libraries.
Krishna Institute of Engineering and Technology (KIET)
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