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✔️ 0️⃣1️⃣Project - Traffic Sign Classification Using Deep Learning in Python/Keras

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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.

✔️ 0️⃣2️⃣Project - Image Classification with CNNs using Keras

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Dataset

CIFAR-10 is a labeled subset of the 80 million tiny images dataset.The 10 different classes represents

1️⃣✈️, 2️⃣🚗, 3️⃣🐦, 4️⃣🐱, 5️⃣🐎, 6️⃣🐕, 7️⃣🐸, 8️⃣🐴, 9️⃣⛵, 1️⃣0️⃣🚚

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.

✔️ 0️⃣3️⃣Project - Facial Expression Recognition with Keras!

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Dataset

Facial Expression Recognition Challenge

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.

✔️ 0️⃣4️⃣Project - Classify Radio Signals from Outer Space using Keras

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Dataset

SETI Dataset

Objectives

• Build and train a convolutional neural network (CNN) using Keras.
• Display results and plot 2D spectrograms with Python in Jupyter Notebook.

✔️ 0️⃣5️⃣Project - Understanding Deepfakes with-Keras Using DCGAN

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Dataset

MNIST

# 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.

✔️ 0️⃣6️⃣Project - Sentiment Analysis with Deep Learning using BERT

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Dataset

SMILE Twitter DATASET

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.

✔️ 0️⃣7️⃣Project - Tumor-Diagnosis Exploratory Data Analysis on Breast Cancer Wisconsin DataSet

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Dataset

The Breast Cancer Diagnostic data is available on the UCI Machine Learning Repository. This database is also available through the UW CS ftp server.

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.

✔️ 0️⃣8️⃣Project - COVID19 Data Analysis Using Python

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DataSet

1. COVID19 dataset published by John Hopkins University.
2. World_Happiness Report
3. 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.

✔️ 0️⃣9️⃣Project - Detecting COVID 19 with Chest X-Ray using PyTorch

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Dataset

COVID-19 Chest X-ray Database

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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