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+} + +.section { + background-color: #fff; + border-radius: 5px; + box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1); + padding: 20px; + margin: 20px 0; +} + +.project { + background-color: #fff; + border: 1px solid #e1e1e1; + border-radius: 5px; + padding: 20px; + margin: 20px 0; + transition: transform 0.3s, box-shadow 0.3s; +} + +.project:hover { + transform: translateY(-5px); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); +} + +h1, h2, h3, h4, h5 { + margin: 0; + color: #333; +} + +a { + color: #3498db; + text-decoration: none; + transition: color 0.3s; +} + +a:hover { + color: #2980b9; +} + +img { + max-width: 100%; + height: auto; + margin: 10px 0; + border-radius: 5px; + box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1); +} + + + +

Hi,

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I am Muhammad Fakhar, a Computer Science student, mainly focused in Data Science and Machine Learning.

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As a Machine Learning practitioner, I have been writing posts and blogs on Data science and ML, here too.

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Thank you for visiting my blog page. I hope you enjoy my articles!

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Thank you.

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End-to-End Projects

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Machine Price Prediction

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In this app, I implemented Random Forest model to generate the price of Machinery based on previous auction data.

+ First Image + Second Image + Third Image + +

Students Performance Predictor

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In this app, I implemented the model to predict the maths score for students based on their profiles.

+ Student Image + +

Journey with fastai Library

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Computer Vision

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Images Regression

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Practice 1: Centre of Head
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In this model, I implemented images regression, to find the coordinates of the centre of the head. It uses MSELoss.

+ Regression Image + +

Sinlge Label Classification

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Practice 1: Dog vs Cat Classifier
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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!

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Practice 2: Player Classifier
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I made a computer vision model, hosted in gradio on huggingfaces, which differs between two categories of people. Ronaldo and Messi. Funny!

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Practice 3: Bear Detector
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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.

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Practice 4: MNIST Digits Classification
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In this model, I just took 2 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.

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Practice 5: Pet Breed Classification
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In this model, I implemented classifying between 37 different cats and dog breeds. This uses softmax activation function, and CrossEntropyLoss.

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Multi-Label Classification

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Practice 1: Predicting Multipple classes
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In this model, I predicted multiple classes present in the picture. This uses sigmoid activation function and BCELossWithLogits loss.

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Tabular Data

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Practice 1: Movies Prediction/Collaborative Filtering
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In this collaborative filtering, I worked on movies dataset, to predict the movie for a user based on his reviews.

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Practice 2: Price Prediction from previous auctions
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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)

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Natural Language Processing

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Practice 1: Generating text for movie reviews/ Classifying the reviews
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In this Language Model, I predicted the text to write the reviews for movies. Also, I classified them as positive review or negative review.

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