Hybrid
Full Time
Should have knowledge in the regression and classification algorithm such as but not limited to Gradient Decent , Linear Regression , RandonForest , Support Vector Machine , K-nearest , Neural Networks DBSCAN , Principal Component Analysis , LDA and Autoencoders , Loss minimization function such as MSE , RMSE , Hinge Loss ect. We are seeking a data science or machine learning professional with strong expertise in both regression and classification algorithms. The ideal candidate should have hands-on experience with a wide range of techniques including, but not limited to: Supervised Learning Algorithms : Gradient Descent, Linear Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Neural Networks. Unsupervised Learning Techniques : DBSCAN, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoders. Loss Functions and Optimization : Proficient in implementing and optimizing loss functions such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Hinge Loss for model performance improvement.
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