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$14 USD / hour
Flag of PAKISTAN
faisalabad, pakistan
$14 USD / hour
It's currently 5:02 AM here
Joined November 7, 2013
0 Recommendations

Abdul B.

@abasit89

 5.0 (2 reviews)
1.8
1.8
100%
100%
$14 USD / hour
Flag of PAKISTAN
faisalabad, pakistan
$14 USD / hour
100%
Jobs Completed
100%
On Budget
100%
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Repeat Hire Rate

Machine Learning | AI |Data Analyst | Python

Working as Machine Learning Engineer and Data Analyst since 4 years. I have experience in following expertise: *** Machine Learning/AI/Deep Learning - Tensorflow, keras, scikit-learn, pandas and computer-vision. - Generic Application based on Python - Machine Learning based on Sci-kit Learn - Tensorflow/Keras based Deep Learning - Image processing based on Computer Vision *** Data Analyst/Excel/Virtual Assistance - Google Data Analytics Professional Certificate, - SQL, Excel sheets, data cleaning, visualizations, Tableau and R programming. - Data Analyst, Data Aggregation, Data Analytics, Data Calculations, Data Cleaning, Data Ethics Data Visualization, Presentations, Problem Solving I strongly believe in client satisfactions by doing my work professionally.
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Portfolio

11663860
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
11155118
Data Analysis Data Science Data Visualization Exploratory Data Analysis Jupyter Notebook Kaggle Machine Learning Predictive Models Python Regression Models Statistical Analysis Tableau
Google Advanced Data Analytics
Data Analysis Data Science Data Visualization Exploratory Data Analysis Jupyter Notebook Kaggle Machine Learning Predictive Models Python Regression Models Statistical Analysis Tableau
Google Advanced Data Analytics
10779129
Data Aggregation Data Analytics Data Calculation Data Cleaning Data Ethics Data Visualization Presentation Problem Solving R Spreadsheets SQL Structured Thinking
Google Data Analytics
Data Aggregation Data Analytics Data Calculation Data Cleaning Data Ethics Data Visualization Presentation Problem Solving R Spreadsheets SQL Structured Thinking
Google Data Analytics
9230554
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python project.
Machine Learning with Python: from Linear Models to Deep Lea
9230538
Deepen my knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. I have learnt how lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow.
Customising your models with TensorFlow 2
9230522
I have completed all five courses of the Deep Learning Specialization. In this Specialization we have learnt about 1-Neural Networks and Deep Learning 2-Improving Deep Neural Networks 3-Structuring Machine Learning Projects 4-Convolutional Neural Networks 5-Sequence Models we have built neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers and learnt how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Theoretical concepts and their application using Python and TensorFlow and also tackled real world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing are experimented. I am familiar with the capabilities, challenges, and consequences of deep learning and ready to participate in the development of leading-edge AI technology.
Deep Learning
11663860
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
Developed a character level model that will be enable to write phrases like Shakespeare poems using RNN Collection of Shakespeare poems named “The Sonnet”. Data Build For 40 timesteps with defined stride. Defined Dictionary Extract all unique character in text. Count and sort them in an order. Allocate unique number to each character from zero (0). Vectorization (One hot encoding) Convert the text into one-hot-encode. Array of zeros expect the unique number of that character. RNN Sequential Model Many to one RNN model. Predict one label after 40 timesteps. LSTM based sequential model. Input layer. LSTM layer. Softmax layer. RMSProp Optimizer. Iterate over For fixed number of epochs by defining Train data. Train labels. Batch size. Sampling of character after every iteration Create a sampled character. Character that has maximum probability. Sampled characters help to identify next character Based upon next character algorithm will write like Shakespeare poem “the sonnet".
Character Level Sequence Model
11155118
Data Analysis Data Science Data Visualization Exploratory Data Analysis Jupyter Notebook Kaggle Machine Learning Predictive Models Python Regression Models Statistical Analysis Tableau
Google Advanced Data Analytics
Data Analysis Data Science Data Visualization Exploratory Data Analysis Jupyter Notebook Kaggle Machine Learning Predictive Models Python Regression Models Statistical Analysis Tableau
Google Advanced Data Analytics
10779129
Data Aggregation Data Analytics Data Calculation Data Cleaning Data Ethics Data Visualization Presentation Problem Solving R Spreadsheets SQL Structured Thinking
Google Data Analytics
Data Aggregation Data Analytics Data Calculation Data Cleaning Data Ethics Data Visualization Presentation Problem Solving R Spreadsheets SQL Structured Thinking
Google Data Analytics
9230554
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python project.
Machine Learning with Python: from Linear Models to Deep Lea
9230538
Deepen my knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. I have learnt how lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow.
Customising your models with TensorFlow 2
9230522
I have completed all five courses of the Deep Learning Specialization. In this Specialization we have learnt about 1-Neural Networks and Deep Learning 2-Improving Deep Neural Networks 3-Structuring Machine Learning Projects 4-Convolutional Neural Networks 5-Sequence Models we have built neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers and learnt how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Theoretical concepts and their application using Python and TensorFlow and also tackled real world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing are experimented. I am familiar with the capabilities, challenges, and consequences of deep learning and ready to participate in the development of leading-edge AI technology.
Deep Learning

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5.0
$60.00 USD
Supervised and GAN-based Techniques for Increasing Fine Texture Details in Super-Resolution
I wanted to take a moment to appreciate for the exceptional work Abdul has done on the project. His dedication and commitment have been truly impressive. Abdul's in-depth knowledge and expertise in Python and other required knowledge have been invaluable throughout the project and has improved the overall quality of our work. His consistent efforts and deep understanding of the project requirements,have met our deadlines and all expectations. I would highly recommend Abdul for any future works.
Python
Machine Learning (ML)
Data Science
Neural Networks
Pytorch
C
Flag of Ruchi J.
@Crypto214
•
1 year ago
5.0
$20.00 USD
Machine Learning Model in Python
Abdul B is a great guy. He finished the task as described
Python
Machine Learning (ML)
Deep Learning
User Avatar
Flag of Princewill O.
@pbolali69
•
2 years ago

Education

MSC Electrical Engineering

University of Engineering and Technology, Lahore, Pakistan 2019 - 2021
(2 years)

Bachelor of Science in Electrical Engineering

COMSATS Institute of Information Technology, Pakistan 2011 - 2015
(4 years)

Qualifications

Machine Learning with Python-From Linear Model to Deep Learning

Massachusetts Institute of Technology, USA
2021
• Learned advanced Machine Learning techniques i.e., Linear Classifiers, generalization, non-linear classifiers, recommender systems, kernel functions, collaborative filtering, Maximum margin hyperplane, perceptron algorithms. • Understood principles behind classification, regression, clustering, and reinforcement learning. • kernel functions like linear kernels, polynomial kernels, and RBF Kernel for SVM • Feature engineering. • Learned deep concepts of NNs, CNNs, RNNs

Customising your models with tensorflow 2

Imperial College of London
2021
• Mastered the techniques to customize the Tensorflow code and developed the fully customized models for dedicated applications. • Adopted lower-level APIs to develop complex model architectures, customized layers, and flexible data flow using Tensorflow (Keras).

Getting Started with Tensorflow 2

Imperial College of London
2021
• Learned fundamental concepts of training, validation, and testing of models using the Tensorflow (Keras) framework. • Learned how to use regularization in Tensorflow

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Data Science 1
Deep Learning 1
Data Entry

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