Machine Learning & Data science Expert
Budget: $8 – $15 USD
I'm seeking a highly skilled and experienced Machine Learning & Data Science expert with both subject matter expertise and the ability to apply it to real-world challenges. The ideal candidate should have significant experience in developing end-to-end Machine Learning & Data Science solutions and being able to navigate the full project life cycle from ideation to implementation.
1. Innovating a novel personalized Federated Learning framework in a cloud-edge architecture, diving deep into terabytes of data from different types of sensors, and developing Artificial Intelligence on Edge.
2. Research and implement Machine Learning algorithms and Deep Learning neural network algorithms to time series, non-intrusive load monitoring, anomaly detection, forecasting, behavioral analysis, and predictive models using Structured and Unstructured data that transform IoT data into actionable information.
3. Work with Data Engineers and Software Developers to implement strategies that optimize data processing pipelines, unbalanced data distributions, and ensure high-quality performance.
4. Bringing creativity and collaborating with domain experts from institutions and stakeholders to seek, understand, and solve real problems in the water economy using Artificial Intelligence
5. Staying up to date with machine learning research, and advances in the field.
6. Performing algorithms include supervised machine learning techniques like., decision trees, bootstrap aggregation, CART, boosting; deep learning like CNN, RNN, LSTM; Unsupervised machine learning techniques like., k-means clustering, principal component analysis, hierarchical clustering; Federated learning algorithms and learning techniques like., Transfer Learning, Active Learning.
7. Apply techniques such as multivariate exponential smoothing, ARIMA, Forecasting, Time Series Analysis, statistical modeling, sentiment analysis, Text Mining to achieve the desired result.
8. Prototype and validate advanced Machine Learning and Deep Learning models and algorithms to transform IoT data into actionable information- "Knowledge of Water”
1. Build machine learning pipelines from inception to maintenance in multiple Machine Learning Libraries and Frameworks, such as Tensorflow, ScikitLearn, PyTorch, NLTK, AutoML, Keras, and other open-source libraries.
9. Develop and implement a data-intensive machine learning platform for IoT, Water Analytics, and Predictive Analytics using Machine Learning Libraries and Frameworks such as Tensorflow, ScikitLearn, PyTorch, NLTK, AutoML, Keras, and other open-source libraries.
10. Provide analysis, reporting, and modeling to internal organizations based on customer data.
11. Collaborating with subject matter experts from institutions, stakeholders, and customer teams to seek, understand, validate, interpret, and correctly use data elements.
1. Innovating a novel personalized Federated Learning framework in a cloud-edge architecture, diving deep into terabytes of data from different types of sensors, and developing Artificial Intelligence on Edge.
2. Research and implement Machine Learning algorithms and Deep Learning neural network algorithms to time series, non-intrusive load monitoring, anomaly detection, forecasting, behavioral analysis, and predictive models using Structured and Unstructured data that transform IoT data into actionable information.
3. Work with Data Engineers and Software Developers to implement strategies that optimize data processing pipelines, unbalanced data distributions, and ensure high-quality performance.
4. Bringing creativity and collaborating with domain experts from institutions and stakeholders to seek, understand, and solve real problems in the water economy using Artificial Intelligence
5. Staying up to date with machine learning research, and advances in the field.
6. Performing algorithms include supervised machine learning techniques like., decision trees, bootstrap aggregation, CART, boosting; deep learning like CNN, RNN, LSTM; Unsupervised machine learning techniques like., k-means clustering, principal component analysis, hierarchical clustering; Federated learning algorithms and learning techniques like., Transfer Learning, Active Learning.
7. Apply techniques such as multivariate exponential smoothing, ARIMA, Forecasting, Time Series Analysis, statistical modeling, sentiment analysis, Text Mining to achieve the desired result.
8. Prototype and validate advanced Machine Learning and Deep Learning models and algorithms to transform IoT data into actionable information- "Knowledge of Water”
1. Build machine learning pipelines from inception to maintenance in multiple Machine Learning Libraries and Frameworks, such as Tensorflow, ScikitLearn, PyTorch, NLTK, AutoML, Keras, and other open-source libraries.
9. Develop and implement a data-intensive machine learning platform for IoT, Water Analytics, and Predictive Analytics using Machine Learning Libraries and Frameworks such as Tensorflow, ScikitLearn, PyTorch, NLTK, AutoML, Keras, and other open-source libraries.
10. Provide analysis, reporting, and modeling to internal organizations based on customer data.
11. Collaborating with subject matter experts from institutions, stakeholders, and customer teams to seek, understand, validate, interpret, and correctly use data elements.