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$7 USD / hora
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Aquí son las 10:12 p. m.
Se unió el julio 24, 2023
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Carlos C.

@charlycs98

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Web Scrapping | App | IoT | AI | ML | Data science

Highly Skilled Engineering Graduate with Expertise in Python, Web Scraping, Machine Learning (sklearn), and Data Analytics Welcome to my profile! I am a recent Engineering graduate with a passion for leveraging cutting-edge technologies to solve complex problems. With a strong foundation in Python programming, web scraping, machine learning using sklearn, and data analytics, I am well-equipped to take on diverse projects in these domains. My skills in Python enable me to develop efficient and robust solutions for various applications. Whether it's building web scrapers to gather data from multiple sources or creating custom automation scripts, I can deliver reliable and tailored solutions to suit your needs. Machine learning is an area I find particularly intriguing. My expertise in sklearn allows me to implement predictive models, perform data analysis, and draw valuable insights from datasets. I am well-versed in applying various algorithms to tackle classification, regression, and clustering tasks. Data analytics is another key strength that allows me to explore and extract meaningful patterns from data. By employing statistical techniques and data visualization tools, I can help you derive actionable insights and make informed decisions. As a new freelancer, I am committed to delivering high-quality work that exceeds your expectations. I am a quick learner, detail-oriented, and dedicated to continuous improvement. My goal is to ensure your satisfaction with the projects we collaborate on. If you have a project related to Python, web scraping, machine learning, or data analytics, I would be thrilled to discuss it further. Let's work together to bring your ideas to life and achieve outstanding results. Feel free to reach out to me, and let's embark on an exciting journey of successful projects together.
Freelancer C Programmers Peru
Ver todo C Programmers en Peru

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Portafolio

11260028
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
11193314
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process
11260028
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
Focuses on the successful implementation of machine learning models in the cloud using the Google Cloud platform. Implementing in the cloud, specifically on GCP, offers several key advantages. By choosing Google Cloud, access to scalable and reliable infrastructure is provided, enabling efficient deployment and simplified management of machine learning models. The GCP platform offers specialized tools and services for model deployment, such as Google AI Platform, which facilitates implementation and monitoring of models in production. The result is a comprehensive solution that combines the power of machine learning with the flexibility and reliability of the cloud. Deploying machine learning models on the GCP platform leads to enhanced operational efficiency, access to advanced data analytics resources, and the creation of more predictive and efficient systems in the mining industry and beyond. In summary, this project showcases how deploying models in the cloud, especially on GCP.
Deploying Machine Learning Models on Google Cloud Platform
11193314
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process
Machine Learning in Mining Process" is a Python-based code project leveraging TensorFlow for ore treatment optimization. It explores silica's extraction and refinement through machine learning models that predict crucial parameters like quantity, quality, and energy efficiency. The project uses Python, a preferred language for machine learning due to its simplicity and extensive libraries. TensorFlow, a Python library, facilitates the creation and training of neural networks to learn patterns in mining data. The project creates models that analyze past silica mining data to predict future trends, optimize operational efficiency, and enhance sustainability. This modern approach transforms traditional ore treatment processes, making mining more predictable and efficient.
Machine Learning in Mining Process

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Experiencia

Java Developer

Freelance
feb 2023 - Presente
As a Junior Java Developer, I have focused on mastering Object-Oriented Programming principles, applying them in various projects over the past [number of years/months]. My deep understanding of OOP concepts such as inheritance, encapsulation, and polymorphism has enabled me to design and build robust, modular, and maintainable applications. Using Java and related frameworks, I have contributed to the development of software that adheres to SOLID principles, ensuring efficiency and scalability.

Database Administrator (DBA)

Freelance
nov 2022 - Presente
Experienced in handling both Structured and Non-Structured Databases, I've leveraged SQL and NoSQL technologies to design, implement, and manage efficient data storage solutions. My expertise encompasses optimizing queries, ensuring data integrity, and adapting to diverse data needs.

Linux Bash Script Developer

Freelance
ago 2022 - Presente
As a Linux Bash Script Developer, I've specialized in writing and optimizing scripts to automate processes and enhance system functionality. My expertise in shell scripting has contributed to improving efficiency, reliability, and maintainability within various Linux environments.

Educación

Bachellor

Universidad Nacional de Ingeniería, Peru 2018 - 2023
(5 años)

Calificaciones

Google Cloud Computing Foundations: Networking

Google Cloud Skills Boost
2023
I learned about virtual private clouds (VPCs), subnets, and load balancing techniques. Through hands-on exercises, I gained expertise in creating and managing secure and scalable network architectures. The course provided insights into connecting on-premises networks to the cloud and optimizing network performance. I now possess the skills to design and implement robust networking solutions to support cloud-based applications and services effectively.

Google Cloud Computing Foundations: Cloud Computing Fundamentals

Google Cloud Skills Boost
2023
Introduced me to the core concepts of cloud computing. I delved into essential topics such as service models (IaaS, PaaS, SaaS), cloud deployment models, and cost management strategies. Through practical examples, I gained a solid understanding of provisioning resources, scaling applications, and optimizing costs in the cloud. This course equipped me with the knowledge to navigate the cloud landscape and make informed decisions when adopting cloud technologies for various business requirements.

Google Cloud Computing Foundations: Infrastructure in Google Cloud

Google Cloud Skills Boost
2023
Immersed me in the fundamentals of cloud technology. I explored Google Cloud Platform's infrastructure components, including virtual machines, storage, and networking. Through hands-on labs, I learned to provision resources, manage data, and ensure scalability and reliability. The course equipped me with insights into cloud architecture, enabling me to design and deploy robust solutions that harness the power of Google's cloud infrastructure for diverse business needs.

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Inicia sesión para comentar cualquier detalle por chat.

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Principales habilidades

C Programming
JavaScript
Python
Linux
Data Processing

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