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

End-to-End Projects

Machine Price Prediction

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

Students Performance Predictor

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

Journey with fastai Library

Computer Vision

Images Regression

Practice 1: Centre of Head

In this model, I implemented images regression, to find the coordinates of the centre of the head. It uses MSELoss.

Sinlge Label Classification

Practice 1: Dog vs Cat Classifier

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!

Practice 2: Player Classifier

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

Practice 3: Bear Detector

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.

Practice 4: MNIST Digits Classification

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.

Practice 5: Pet Breed Classification

In this model, I implemented classifying between 37 different cats and dog breeds. This uses softmax activation function, and CrossEntropyLoss.

Multi-Label Classification

Practice 1: Predicting Multipple classes

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

Tabular Data

Practice 1: Movies Prediction/Collaborative Filtering

In this collaborative friltering, I worked on movies dataset, to predict the movie for a user based on his reviews.

Practice 2: Price Prediction from previous auctions

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)

Natural Language Processing

Practice 1: Generating text for movie reviews/ Classifying the reviews

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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