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-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](https://github.com/fakhar-iqbal/MachineryPriceEstimator_End_to_End_Project)
-In this app, I implemented Random Forest model to generate the price of Machinery based on previous auction data. 
-
-![](/images/first.png)
-![](/images/second.png)
-![](/images/third.png)
-
-
-# [Students Performance Predictor](https://github.com/fakhar-iqbal/Student_Performance_Predictor_End_to_End_Project)
-In this app, I implemented the model to predict the maths score for students based on their profiles. 
-
-![](/images/student.png)
-
-
-# Journey with fastai Library
-
-# Computer Vision
-
-## Images Regression
-
-### [Practice 1: Centre of Head](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/ImagesRegression.ipynb)
-In this model, I implemented images regression, to find the coordinates of the centre of the head. It uses MSELoss. 
-
-![](/images/regression.png)
-
-## Sinlge Label Classification
-
-### [Practice 1: Dog vs Cat Classifier](https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/Dog_vs_CatApp.ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/PlayerClassifier.ipynb)
-I made a computer vision model, hosted in gradio on huggingfaces, which differs between two categories of people. Ronaldo and Messi. Funny! 
-
-![](/images/messi.png)
-
-
-### [Practice 3: Bear Detector](https://github.com/fakhar-iqbal/FastaiImplementations/tree/main/ComputerVision/BearClassifierPrototype%20.ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/DigitClassifierNNfromScratch.ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/PetBreedsNN.ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/ComputerVision/MultiLabelClassification.ipynb)
-In this model, I predicted multiple classes present in the picture. This uses sigmoid activation function and BCELossWithLogits loss. 
-
-![](/images/multilabel.png)
-
-
-# Tabular Data
-
-### [Practice 1: Movies Prediction/Collaborative Filtering](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/Collab_filtering_TabularData/CollaborativeFiltering(onMovies).ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/Collab_filtering_TabularData/TabularDataModel.ipynb)
-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](https://github.com/fakhar-iqbal/FastaiImplementations/blob/main/NLP/LanguageModel_NLP_final.ipynb)
-In this Language Model, I predicted the text to write the reviews for movies. Also, I classified them as positive review or negative review. 
-