Machine Learning Internship

Selected intern's day-to-day responsibilities include:

1. Research, identify, and understand business requirements to identify opportunities for applying predictive modeling and machine learning techniques
2. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project objectives and requirements
3. Design and implement predictive modeling and machine learning solutions, including data preprocessing, feature engineering, and model selection
4. Develop and deploy predictive software applications into production, leveraging Python (or similar scripting languages) for efficient development
5. Expertise in training and fine-tuning LLMs on diverse datasets, ensuring superior performance, and adapting models for specific applications and domains.
6. Work with cloud-native solutions on platforms such as AWS, GCP, or Azure for scalability and flexibility
7. Apply a range of machine learning algorithms for different tasks, including classification (SVM, decision tree, random forest, neural network), regression (linear, polynomial, logistic, etc), and graph theory (network analytics)
8. Utilize classical optimization techniques such as gradient descent and others for model optimization and parameter tuning
9. Utilize deep learning techniques, including LSTM, convolutional neural networks (CNN), and recurrent neural networks (RNN), for tasks such as natural language processing, image recognition, or time series analysis
10. Experience with training ML models using Pytorch or tensorflow.

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