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 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.
Learn how you to perform speech synthesis by converting text to speech both online and offline using gTTS and pyttsx3 libraries 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 YOLOv8 object detection for real-time object detection, recognition and localization in Python using OpenCV, Ultralytics 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.
Learn how to Use Tesseract OCR library and pytesseract wrapper for optical character recognition (OCR) to convert text in images into digital text in Python.
Learning how to use Speech Recognition Python library for performing speech recognition to convert audio speech to 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.
Performing face detection using both Haar Cascades and Single Shot MultiBox Detector methods with OpenCV's dnn module in Python.