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<!DOCTYPE html> | ||
<html> | ||
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<head> | ||
<title>Fakhar Iqbal</title> | ||
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<link rel="stylesheet" href="styles.css"> | ||
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</head> | ||
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<body> | ||
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<main> | ||
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<div class="intro container"> | ||
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<h1>Hi, I'm Muhammad Fakhar</h1> | ||
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<p>As a Machine Learning practitioner, I have been writing posts and blogs on Data science and ML, here too. Thank you for visiting my blog page. I hope you enjoy my articles!</p> | ||
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</div> | ||
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<div class="projects container"> | ||
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<h2>End-to-End Projects</h2> | ||
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<div class="project"> | ||
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<h3><a href="https://github.com/fakhar-iqbal/MachineryPriceEstimator_End_to_End_Project">Machine Price Prediction</a></h3> | ||
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<p>In this app, I implemented Random Forest model to generate the price of Machinery based on previous auction data.</p> | ||
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<img class="image-small" src="/images/first.png"> | ||
<img class="image-small" src="/images/second.png"> | ||
<img class="image-small" src="/images/third.png"> | ||
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</div> | ||
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<div class="project"> | ||
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<h3><a href="https://github.com/fakhar-iqbal/Student_Performance_Predictor_End_to_End_Project">Students Performance Predictor</a></h3> | ||
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<p>In this app, I implemented the model to predict the maths score for students based on their profiles.</p> | ||
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<img class="image-small" src="/images/student.png"> | ||
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</div> | ||
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</div> | ||
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<div class="journey container"> | ||
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<h2>Journey with fastai Library</h2> | ||
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<h3>Computer Vision</h3> | ||
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<div class="project"> | ||
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<h4>Images Regression</h4> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/ImagesRegression.ipynb">Practice 1: Centre of Head</a></p> | ||
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<p>In this model, I implemented images regression, to find the coordinates of the centre of the head. It uses MSELoss.</p> | ||
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<img class="image-small" src="/images/regression.png"> | ||
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</div> | ||
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<div class="project"> | ||
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<h4>Sinlge Label Classification</h4> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/Dog_vs_CatApp.ipynb">Practice 1: Dog vs Cat Classifier</a></p> | ||
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<p>I made a computer vision model hosted in gradio, on huggingfaces, in Fastai, which classifies between dogs and cats. Have a look in my repo! Click the title!</p> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/PlayerClassifier.ipynb">Practice 2: Player Classifier</a></p> | ||
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<p>I made a computer vision model, hosted in gradio on huggingfaces, which differs between two categories of people. Ronaldo and Messi. Funny!</p> | ||
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<img class="image-small" src="/images/messi.png"> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/BearClassifierPrototype .ipynb">Practice 3: Bear Detector</a></p> | ||
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<p>In this model, I classified between 3 types of bears, black, teddy and grizzly. With 100% accuracy. This app prototype is also hosted on huggingfaces spaces.</p> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/DigitClassifierNNfromScratch.ipynb">Practice 4: MNIST Digits Classification</a></p> | ||
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<p>In this model, I just took2 digits, as provided as Samples in the dataset. I implemented the Neural Network from scratch to classify the single label. This uses softmax activation function and CrossEntropyLoss.</p> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/PetBreedsNN.ipynb">Practice 5: Pet Breed Classification</a></p> | ||
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<p>In this model, I implemented classifying between 37 different cats and dog breeds. This uses softmax activation function, and CrossEntropyLoss.</p> | ||
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</div> | ||
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<div class="project"> | ||
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<h4>Multi-Label Classification</h4> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/MultiLabelClassification.ipynb">Practice 1: Predicting Multipple classes</a></p> | ||
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<p>In this model, I predicted multiple classes present in the picture. This uses sigmoid activation function and BCELossWithLogits loss.</p> | ||
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<img class="image-small" src="/images/multilabel.png"> | ||
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</div> | ||
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</div> | ||
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<div class="tabular container"> | ||
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<h3>Tabular Data</h3> | ||
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<div class="project"> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/Collab_filtering_TabularData/CollaborativeFiltering(onMovies).ipynb">Practice 1: Movies Prediction/Collaborative Filtering</a></p> | ||
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<p>In this collaborative friltering, I worked on movies dataset, to predict the movie for a user based on his reviews.</p> | ||
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</div> | ||
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<div class="project"> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/Collab_filtering_TabularData/TabularDataModel.ipynb">Practice 2: Price Prediction from previous auctions</a></p> | ||
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<p>In this model. based on previous prices of machines on auctions, I predicted the price for new machinery. I used the data from kaggle.(bluebook for bulldozers)</p> | ||
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</div> | ||
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</div> | ||
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<div class="nlp container"> | ||
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<h3>Natural Language Processing</h3> | ||
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<div class="project"> | ||
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<p><a href="https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/NLP/LanguageModel_NLP_final.ipynb">Practice 1: Generating text for movie reviews/ Classifying the reviews</a></p> | ||
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<p>In this Language Model, I predicted the text to write the reviews for movies. Also, I classified them as positive review or negative review.</p> | ||
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</div> | ||
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</div> | ||
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</main> | ||
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</body> | ||
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</html> |
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body { | ||
background-color: #f9f9f9; | ||
} | ||
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main { | ||
max-width: 800px; | ||
margin: 0 auto; | ||
padding: 20px; | ||
} | ||
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.container { | ||
max-width: 600px; | ||
margin: 0 auto; | ||
} | ||
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.image-small { | ||
width: 300px; | ||
} | ||
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@media (max-width: 600px) { | ||
.image-small { | ||
width: 100%; | ||
} | ||
} | ||
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a { | ||
color: #3f51b5; | ||
} | ||
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/* Typography */ | ||
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body { | ||
font-family: 'Open Sans', sans-serif; | ||
} | ||
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h1, h2, h3, h4 { | ||
font-family: 'Montserrat', sans-serif; | ||
} | ||
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/* Header */ | ||
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.intro { | ||
background-image: linear-gradient(#fff, #f9f9f9); | ||
} | ||
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/* Projects */ | ||
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.projects { | ||
background-color: #fff; | ||
box-shadow: 0 4px 12px rgba(0,0,0,0.1); | ||
} | ||
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.project { | ||
background-color: #fdfdfd; | ||
} | ||
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/* Smooth scroll */ | ||
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html { | ||
scroll-behavior: smooth; | ||
} | ||
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/* Transitions */ | ||
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a { | ||
transition: color 0.3s; | ||
} | ||
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.project { | ||
transition: transform 0.2s; | ||
} | ||
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.project:hover { | ||
transform: scale(1.01); | ||
} |