Learn how to build machine learning and deep learning models for many purposes in Python using popular frameworks such as TensorFlow, PyTorch, Keras and OpenCV.
Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python.
Learn how to build a deep learning malaria detection model to classify cell images to either infected or not infected with Malaria Tensorflow 2 and Keras API in Python.
Learn how to handle stock prices in Python, understand the candles prices format (OHLC), plotting them using candlestick charts as well as learning to use many technical indicators using stockstats library in Python.
Learn how to build a deep learning model that is able to detect and recognize your gender just by your voice tone using Tensorflow framework in Python.
Blurring and anonymizing faces in images and videos after performing face detection using OpenCV library in Python.
Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python
Using the state-of-the-art YOLOv3 object detection for real-time object detection, recognition and localization in Python using OpenCV and PyTorch.
Predicting different stock prices using Long Short-Term Memory Recurrent Neural Network in Python using TensorFlow 2 and Keras.
Using K-Means Clustering unsupervised machine learning algorithm to segment different parts of an image using OpenCV in Python.
Learning how to detect contours in images for image segmentation, shape analysis and object detection and recognition using OpenCV in Python.
Using Tesseract OCR library and pytesseract wrapper for optical character recognition (OCR) to convert text in images into digital text in Python.
Learn what is transfer learning and how to use pre trained MobileNet model for better performance to classify flowers using TensorFlow in Python.
Building a deep learning model to generate human readable text using Recurrent Neural Networks (RNNs) and LSTM with TensorFlow and Keras frameworks in Python.
Building and training a model that classifies CIFAR-10 dataset images that were loaded using Tensorflow Datasets which consists of airplanes, dogs, cats and other 7 objects using Tensorflow 2 and Keras libraries in Python.
Classifying emails (spam or not spam) with GloVe embedding vectors and RNN/LSTM units using Keras in Python.
Building a Speech Emotion Recognition system that detects emotion from human speech tone using Scikit-learn library in Python