Machine Learning Intern
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Stipend: ₹Unpaid
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Full time
The Machine Learning Internship at WN Infotech will be responsible for assisting in the development and implementation of machine learning models and algorithms. This role offers a unique opportunity to gain practical experience in the field of machine le
Responsibilities
Model Development: Assist in the design, development, and optimization of machine learning models using libraries such as TensorFlow, PyTorch, and Scikit-learn. Work on tasks like regression, classification, clustering, and deep learning.
Data Preparation: Collect, preprocess, and clean datasets to prepare them for analysis and model training. Perform data augmentation and feature engineering to enhance model performance.
Training and Evaluation: Train machine learning models and evaluate their performance using various metrics. Optimize models for accuracy, efficiency, and scalability.
Algorithm Implementation: Implement machine learning algorithms for various business applications. Customize algorithms to meet specific project requirements.
Collaboration: Work closely with data scientists, engineers, and software developers to integrate machine learning models into production systems. Provide support in deploying and maintaining machine learning solutions.
Documentation and Reporting: Document machine learning workflows and model development processes. Prepare detailed reports on model performance, highlighting key insights and areas for improvement.
Continuous Learning: Stay updated with the latest advancements in machine learning research and technologies. Continuously seek to improve skills and methodologies by exploring new tools and techniques.
Required Skills/Qualifications
Model Development: Assist in the design, development, and optimization of machine learning models using libraries such as TensorFlow, PyTorch, and Scikit-learn. Work on tasks like regression, classification, clustering, and deep learning.
Data Preparation: Collect, preprocess, and clean datasets to prepare them for analysis and model training. Perform data augmentation and feature engineering to enhance model performance.
Training and Evaluation: Train machine learning models and evaluate their performance using various metrics. Optimize models for accuracy, efficiency, and scalability.
Algorithm Implementation: Implement machine learning algorithms for various business applications. Customize algorithms to meet specific project requirements.
Collaboration: Work closely with data scientists, engineers, and software developers to integrate machine learning models into production systems. Provide support in deploying and maintaining machine learning solutions.
Documentation and Reporting: Document machine learning workflows and model development processes. Prepare detailed reports on model performance, highlighting key insights and areas for improvement.
Continuous Learning: Stay updated with the latest advancements in machine learning research and technologies. Continuously seek to improve skills and methodologies by exploring new tools and techniques.
Eligibility Criteria
Educational Background: Currently pursuing or recently completed a degree in Data Science, Computer Science, Mathematics, or a related field. Relevant coursework or certifications in machine learning and data science are highly desirable.
Coursework or Experience: Prior coursework or practical experience in machine learning, data analysis, and model development. Experience with projects involving large datasets and complex algorithms is advantageous.
Technical Proficiency: Demonstrated proficiency in using Python for machine learning tasks. Knowledge of SQL and experience with data manipulation in databases is beneficial.
Benefits/Perks
Mentorship and Learning: Receive mentorship from experienced machine learning experts and data scientists, gaining valuable insights into industry practices and advanced techniques. Opportunities for professional development and growth through continuous learning.
Hands-on Experience: Gain practical experience working with real-world datasets and applying machine learning methods to derive actionable insights. Develop a strong foundation in machine learning and data science for business applications.
Professional Growth: Opportunity to enhance technical skills and build a strong portfolio of machine learning projects. Potential for a full-time position based on performance and organizational needs. Access to training programs, workshops, and conferences.
Networking: Engage with professionals across various departments, expanding professional networks and building relationships within the data science and machine learning communities. Opportunities to collaborate with leading experts and industry leaders.
Flexible Working Hours: Enjoy a flexible working schedule that accommodates academic commitments and promotes work-life balance. Option to work remotely or on-site as per project requirements.
Stipend: Competitive stipend to support the intern during the internship period. Financial support for additional learning resources and certification programs.
Access to Resources: Utilize company resources, including training materials, software tools, and industry research, to enhance learning and development. Access to a library of machine learning and data science resources.
Company Details
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