Python Developer with AI / Machine Learning Experience

Luis Daniel Pambid

(0)

Starting at

/hr

About this service

Summary

What problem are you solving for clients?

I provide advanced machine learning solutions, full-stack development, and automation to solve complex technical challenges, streamline processes, and innovate systems that meet and exceed client expectations.

Who is your ideal client?

My ideal client is a company or individual seeking cutting-edge machine learning expertise combined with full-stack development and automation solutions. They value efficiency, innovation, and a collaborative approach to problem-solving.

Why should a client choose you over another freelancer?

Clients should choose me for my unique blend of skills as a licensed Electronics Engineer, Cisco-certified professional, and full-stack developer with a deep focus on machine learning and automation. My diverse expertise and proven track record of delivering exceptional results distinguish me from other freelancers.

What is your specialty or niche?

I specialize in Machine Learning and Full-Stack Development, with extensive experience in AI/ML applications, automation, and network engineering. My tech stack includes Python, Azure, React JS, Node JS, PyTorch, TensorFlow and Keras.

Why do clients keep coming back to you?

Clients return because of my consistent delivery of high-quality results, commitment to staying current with the latest technologies, and strong work ethic. They appreciate my proactive approach, excellent communication, and dedication to exceeding their expectations.

What are your greatest professional strengths?

• Machine Learning Expertise: Deep understanding and application of AI/ML technologies.

• Technical Proficiency: Extensive knowledge in programming languages, automation, and network engineering.

• Problem-Solving: Ability to tackle complex technical challenges and develop innovative solutions.

• Adaptability: Quick learner who stays updated with evolving technologies.

• Leadership: Efficiently leading teams of both Network and Software Engineers.

• Reliability: Consistently delivering high-quality work on time and exceeding expectations.

What positive feedback have you received from clients?

Clients have praised my exceptional machine learning expertise, strong work ethics, and ability to lead and complete projects efficiently. I have been recognized for my excellent work on various projects and have received multiple awards for consistently exceeding customer expectations.

What's included

Preprocessed and Cleaned Training Data
The raw data collected for the project will be preprocessed and cleaned to remove any inconsistencies, errors, or irrelevant information. This ensures that the data is suitable for training the machine learning models.
Feature-Engineered and Chosen Data
Features relevant to the problem at hand will be identified and engineered from the dataset. This involves selecting the right attributes that contribute to the predictive power of the model.
Trained Machine Learning Models
Using various machine learning techniques such as random forests, gradient boosting, neural networks, etc., models will be trained on the prepared data. These models are tailored to perform well on the specific task defined by the project.
Model Performance Evaluation
The performance of the trained models will be evaluated using metrics such as accuracy, F1 score, precision, recall, etc. This assessment helps in determining how well the models generalize to unseen data.
Optimized Machine Learning Model
Efforts will be made to optimize the models for both performance and efficiency. This could involve tuning hyperparameters, reducing overfitting, or improving computational efficiency.
Documentation
Comprehensive documentation will be provided detailing the machine learning model, its architecture, the preprocessing steps, feature engineering process, and how it integrates into the client's platform or application. This documentation serves as a valuable resource for future maintenance and enhancements.
Training
The client will receive training on how to use the machine learning model effectively within their platform or application. This includes understanding how to interact with the model, interpret its outputs, and troubleshoot common issues.
Deployed and Integrated Machine Learning Model
Finally, the optimized and tested machine learning model will be deployed and integrated into the client's platform or application. This ensures that the model is ready to be used in a real-world setting.