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Unser Honorar
$40 USD / Stunde
Flagge von UNITED KINGDOM
dagenham, united kingdom
Unser Honorar
$40 USD / Stunde
Es ist derzeit 12:14 PM hier
Beigetreten am April 2, 2024
1 Recommendation

Techionik

@hassansuhail93

monthly-level-four.svg 5,0 (4 Bewertungen)
3,5
3,5
100%
100%
$40 USD / Stunde
Flagge von UNITED KINGDOM
dagenham, united kingdom
$40 USD / Stunde
75 %
Abgeschlossene Arbeiten
100 %
Budgetgerecht
100 %
Pünktlich
N/A
Wiedereinstellungsrate

FROM EXCELLENCE TO INNOVATION (WEB, AI)

Featured-Bewertung
"Quick and easy to work with! Happy to hire again!"
- Jonas T.
Welcome to Techionik, your premier destination for cutting-edge technological solutions and professional development services! Our Expertise: â—‰AI & ML Pioneers: Techionik pioneers in leveraging cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) technologies. Our expertise spans advanced models and neural networks, enabling us to deliver solutions such as intelligent automation, predictive analytics, and natural language processing. â—‰Web Development Experts: With a focus on crafting tailored solutions, Techionik excels in web development using technologies like PHP, JavaScript, React, Vue, and more. From dynamic websites to scalable web applications, we ensure your digital presence stands out with bespoke solutions. â—‰Other Services: Techionik excels in web scraping, extracting valuable data, and tailored digital marketing solutions using SEO, social media, PPC, and content marketing, enhancing online presence and driving engagement effectively.
Freelancer PHP Developers United Kingdom
Alle PHP Developers in United Kingdom ansehen

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Portfolio

11811414
LLaMA (Large Language Model for Meta AI) represents an innovative approach to leveraging large language models for research purposes and question-answering tasks. By employing LLaMA in a research-based context, scientists and researchers can utilize its vast knowledge base and advanced language understanding capabilities to explore complex scientific topics, analyze datasets, and generate insights.
Large Language Model with Meta AI
LLaMA (Large Language Model for Meta AI) represents an innovative approach to leveraging large language models for research purposes and question-answering tasks. By employing LLaMA in a research-based context, scientists and researchers can utilize its vast knowledge base and advanced language understanding capabilities to explore complex scientific topics, analyze datasets, and generate insights.
Large Language Model with Meta AI
11811412
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
11811409
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
11811407
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
11811405
Creating a chatbot that can answer questions by parsing Wikipedia content using NLTK (Natural Language Toolkit) and Selenium for web scraping involves to parse the Wikipedia Context
Natural Language Processing with Natural Language Tool Kit
Creating a chatbot that can answer questions by parsing Wikipedia content using NLTK (Natural Language Toolkit) and Selenium for web scraping involves to parse the Wikipedia Context
Natural Language Processing with Natural Language Tool Kit
11811403
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes
11811414
LLaMA (Large Language Model for Meta AI) represents an innovative approach to leveraging large language models for research purposes and question-answering tasks. By employing LLaMA in a research-based context, scientists and researchers can utilize its vast knowledge base and advanced language understanding capabilities to explore complex scientific topics, analyze datasets, and generate insights.
Large Language Model with Meta AI
LLaMA (Large Language Model for Meta AI) represents an innovative approach to leveraging large language models for research purposes and question-answering tasks. By employing LLaMA in a research-based context, scientists and researchers can utilize its vast knowledge base and advanced language understanding capabilities to explore complex scientific topics, analyze datasets, and generate insights.
Large Language Model with Meta AI
11811412
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
Fine-tuning a Large Language Model (LLM) for science exams involves adapting the model's knowledge and capabilities to comprehend and answer questions related to various scientific disciplines. By training the LLM on a dataset consisting of science exam questions along with their corresponding answers or solutions, the model can learn to understand the nuances of scientific concepts and effectively provide accurate responses to exam-style queries.
Large Language Model
11811409
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
BERT (Bidirectional Encoder Representations from Transformers) is a powerful pre-trained language model developed by Google. Fine-tuning a BERT model for sentiment analysis involves leveraging its contextual understanding of language to classify text based on sentiment. By training BERT on a labeled dataset of sentiment-labeled text, the model learns to accurately predict the sentiment (positive, negative, or neutral) of new text inputs. Fine-tuning BERT for sentiment analysis enables it to capture nuanced contextual information and improve the accuracy of sentiment classification tasks.
Bidirectional Encoder Representations from Transformers
11811407
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
Analyzing a football playground using YOLOv8 (You Only Look Once version 8) for object detection and keypoint detection involves several steps. YOLOv8 is a state-of-the-art object detection algorithm known for its speed and accuracy.
Computer Vision and Object Detection
11811405
Creating a chatbot that can answer questions by parsing Wikipedia content using NLTK (Natural Language Toolkit) and Selenium for web scraping involves to parse the Wikipedia Context
Natural Language Processing with Natural Language Tool Kit
Creating a chatbot that can answer questions by parsing Wikipedia content using NLTK (Natural Language Toolkit) and Selenium for web scraping involves to parse the Wikipedia Context
Natural Language Processing with Natural Language Tool Kit
11811403
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes
Naive Bayes is a simple but powerful probabilistic classifier based on Bayes' theorem with the "naive" assumption of independence between features. It's commonly used for classification tasks. In the context of predicting Iris species, Naive Bayes can be applied by assuming independence between the features (e.g., sepal length, sepal width, petal length, petal width) given the class labels (Iris species)
Regression With Naive Bayes

Bewertungen

Änderungen gespeichert
Zeigt 1 - 4 von 4 Bewertungen
Bewertungen filtern nach:
5,0
£135,00 GBP
Customizable eCommerce Site for Diverse Products
Had not issues woring with Hassan, great communication and got the job done, i was sick at the time of the project so sometimes it took me 2-3 days to reply and he was very patient and understanding, will defiently use in the future thanks allot.
PHP
Website Design
Graphic Design
eCommerce
HTML
Avatar des Nutzers
Flagge von Delroy K.
@DSKENTERPRISELTD
•
vor 6 Stunden
1,0
$350,00 CAD
Finalize almost complete source code - 05/04/2024 10:43 EDT
they did not follow through with the deadline nor communicate at all .i would be hopeful to work with them so they can show they can comeplete this task
PHP
C Programming
JavaScript
Python
C++ Programming
S
Flagge von Justin G.
@Spyder403
•
vor 7 Tagen
5,0
$140,00 USD
Python Script for Retraining MobileBERT in Tensorflow
Quick and easy to work with! Happy to hire again!
Python
Machine Learning (ML)
Tensorflow
Avatar des Nutzers
Flagge von Jonas T.
@Jones1290
•
vor 13 Tagen
5,0
$254,00 USD
Advanced Customizable Photo Editor Development
Techno was willing to work with my project and other parts of my project. He is efficient and easy to work with. Will hire again!
Image Processing
Image Recognition
A
Flagge von Jeramy C.
@Avtechman91
•
vor 23 Tagen

Erfahrung

CEO

Techionik Ltd
Juni 2023 - Aktuell
Get professionals for ML, AI solutions and present your business online with Techionik!

Ausbildung

Master's of Business Administration

Birmingham City University, United Kingdom 2017 - 2019
(2 Jahre)

Bachelor's Accounting and Finance

Birmingham City University, United Kingdom 2014 - 2017
(3 Jahre)

Qualifikationen

Diversity, Inclusion, and Belonging

LinkedIn
2020
Certified in Diversity, Inclusion, and Belonging from LinkedIn, skilled in fostering environments that embrace and celebrate diverse perspectives. Equipped with strategies to cultivate inclusive cultures, promote equity, and foster a sense of belonging for all individuals within organizational settings.

Proven Tips For Managing Your Time

LinkedIn
2021
Certified in Time Management Mastery by LinkedIn, adept in implementing proven strategies to optimize productivity and efficiency. Equipped with techniques to prioritize tasks, streamline workflows, and maximize the use of available time resources for enhanced personal and professional effectiveness.

Learning LinkedIn Sale Navigator

LinkedIn
2021
Certified in LinkedIn Sales Navigator Mastery, proficient in navigating the digital landscape to identify and capitalize on strategic sales opportunities. Skilled in leveraging advanced tools and insights to streamline prospecting efforts and cultivate meaningful client relationships.

Kontaktieren Sie Techionik bezüglich Ihres Jobs

Melden Sie sich an, um Details im Chat zu besprechen.

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Top-Fähigkeiten

PHP 1
Python 1
Website Design 1
Graphic Design 1
Machine Learning (ML) 1

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