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https://github.com/mohd-faizy/datascience-projects
Projects : DataScience, Artificial intelligence, Machine learning, Deep Learning
https://github.com/mohd-faizy/datascience-projects
autoencoder bert-model classification-algorithm convolutional-neural-network convolutional-neural-networks dataset deeplearning generative-adversarial-network keras machine-learning-projects machinelearning natural-language-processing rainforcement-learning sentiment-analysis tensorflow
Last synced: 26 days ago
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Projects : DataScience, Artificial intelligence, Machine learning, Deep Learning
- Host: GitHub
- URL: https://github.com/mohd-faizy/datascience-projects
- Owner: mohd-faizy
- License: mit
- Created: 2020-11-04T20:24:42.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-12T22:46:55.000Z (about 2 years ago)
- Last Synced: 2023-04-08T21:39:47.039Z (over 1 year ago)
- Topics: autoencoder, bert-model, classification-algorithm, convolutional-neural-network, convolutional-neural-networks, dataset, deeplearning, generative-adversarial-network, keras, machine-learning-projects, machinelearning, natural-language-processing, rainforcement-learning, sentiment-analysis, tensorflow
- Homepage:
- Size: 9.05 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
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[](https://github.com/mohd-faizy/DataScience-Projects/blob/main/LICENSE)
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## :heavy_check_mark: :zero::one:**Project - Traffic Sign Classification Using Deep Learning in Python/Keras**
> [Click here](https://github.com/mohd-faizy/01P_Project_Deep_Learning_for_Traffic_Sign_Classification)
### Objectives
> - Understand the theory and intuition behind Deep Learning and Convolutional Neural Networks (CNNs).
> - Import Key python libraries, dataset and perform image visualization.
> - Perform image normalization and convert images from color-scaled to gray-scaled.
> - Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a back-end.
> - Compile and fit Deep Convolutional Neural Network model to training data.
> - Assess the performance of trained Convolutional Neural Network model and ensure its generalization using various KPIs.
## :heavy_check_mark: :zero::two:**Project - Image Classification with CNNs using Keras**
> [Click here](https://github.com/mohd-faizy/02P_Project_Image_Classification_with_CNNs_using_Keras)
### Dataset
> [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) is a labeled subset of the 80 million tiny images dataset.The 10 different classes represents
:one::airplane:, :two::car:, :three::bird:, :four::cat:, :five::racehorse:, :six::dog2:, :seven::frog:, :eight::horse:, :nine::sailboat:, :one::zero::truck:
Training a CNN in Keras with a TensorFlow backend to solve Image Classification problems
### Objectives
> - Understand how to create convolutional neural networks in Keras.
> - Be able to train convolutional neural networks to solve image classification problems.
## :heavy_check_mark: :zero::three:**Project - Facial Expression Recognition with Keras!**
> [Click here](https://github.com/mohd-faizy/03P_Facial_Expression_Recoginition)
### Dataset
> [**Facial Expression Recognition Challenge**](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)
### Objectives
> - Develop a facial expression recognition model in Keras.
> - Build and train a convolutional neural network (CNN).
> - Deploy the trained model to a web interface with Flask
> - Apply the model to real-time video streams and image data.
## :heavy_check_mark: :zero::four:**Project - Classify Radio Signals from Outer Space using Keras**
> [Click here](https://github.com/mohd-faizy/04P-Classify-Radio-Signals-from-Outer-Space-using-Keras)
## **Dataset**
> [**SETI Dataset**](https://drive.google.com/file/d/1R2BlsYydirhMmf89_D1imOT5aVvkXHi2/view?usp=sharing)
### Objectives
> - Build and train a convolutional neural network (CNN) using Keras.
> - Display results and plot 2D spectrograms with Python in Jupyter Notebook.
## :heavy_check_mark: :zero::five:**Project - Understanding Deepfakes with-Keras Using DCGAN**
> [Click here](https://github.com/mohd-faizy/05P_Understanding_Deepfakes_with_Keras_Using_DCGAN)
## **Dataset**
> **MNIST**
```python
# Downloding the dataset
(x_train, y_train), (x_test, y_test) = tfutils.datasets.mnist.load_data(one_hot=False)
```
### Objectives
> - Implement a Deep Convolutional Generative Adversarial Network (DCGAN).
> - Train a DCGAN to synthesize realistic looking images.
## :heavy_check_mark: :zero::six:**Project - Sentiment Analysis with Deep Learning using BERT**
> [Click here](https://github.com/mohd-faizy/06P_Sentiment-Analysis-With-Deep-Learning-Using-BERT)
## **Dataset**
> [**SMILE Twitter DATASET**](https://doi.org/10.6084/m9.figshare.3187909.v2)
### Objectives
> - To Understand what **Sentiment Analysis** is, and how to approach the problem from a neural network perspective.
> - Loading in pretrained BERT with custom output layer.
> - Train and evaluate finetuned BERT architecture on Sentiment Analysis.
## :heavy_check_mark: :zero::seven:**Project - Tumor-Diagnosis Exploratory Data Analysis on Breast Cancer Wisconsin DataSet**
> [Click here](https://github.com/mohd-faizy/07P_Tumor-Diagnosis-Exploratory-Data-Analysis-on-Breast-Cancer-Wisconsin-DataSet)
## __Dataset__
> The [Breast Cancer Diagnostic data](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) is available on the UCI Machine Learning Repository. This database is also available through the [UW CS ftp server](http://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/WDBC/).
### Objectives
> - Produce data visualizations with Seaborn on Breast Cancer Diagnostic data.
> - Apply graphical techniques used in exploratory data analysis (EDA).
> - Use differenting Machine Learning Algorithms like **KNN's, PCA, RF & SVM** for predicting the outcome.
## :heavy_check_mark: :zero::eight:**Project - COVID19 Data Analysis Using Python**
> [Click here](https://github.com/mohd-faizy/08P_COVID19_Data_Analysis_Using_Python)
## DataSet
>1. [COVID19 dataset](https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) published by John Hopkins University.
>2. [World_Happiness Report](https://github.com/mohd-faizy/08P_COVID19_Data_Analysis_Using_Python/blob/master/world_happiness_report_2019.csv)
>3. [CSSEGISandData](https://github.com/CSSEGISandData)
### Objectives
In this Project we are going to work with COVID19 dataset, published by John Hopkins University, which consist of the data related to cumulative number of confirmed cases, per day, in each Country. Also we have another dataset consist of various life factors, scored by the people living in each country around the globe. We are going to merge these two datasets to see if there is any relationship between the spread of the the virus in a country and how happy people are, living in that country.
> - Learn the steps, needed to be taken to prepare our data sources for an analysis.
> - Learn how to look at our data to find a good measure to establish our analysis based upon.
> - Learn to visualize the result of our analysis.
## :heavy_check_mark: :zero::nine:**Project - Detecting COVID 19 with Chest X-Ray using PyTorch**
> [Click here](https://github.com/mohd-faizy/09P_Detecting_COVID_19_with_Chest_X-Ray_using_PyTorch)
## Dataset
> [**COVID-19 Chest X-ray Database**](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/download)
### Objectives
Training __Convolutional Neural Networks(CNN)__ to classify Chest X-Ray scans with reasonably high accuracy.
> - Create custom Dataset and DataLoader in PyTorch.
> - Train a ResNet-18 model in PyTorch to perform Image Classification.
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[Portfolio]: https://mohdfaizy.com/
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