International ATITAcademy Int ’ l. Amman, Jor dan and Bochum, Germany WhatsApp: +962795037290. Email: info@atitgroup.com Course Outline Image and Video Processing and Machine Learning with Python: A C omplete Practical Course for Researchers [ Online ] Mentor Qais Yousef , Ph.D. in Systems Optimization and Applied Neuroscience , with 10 + years of experience in professional and a cademic fields WhatsApp: +962795037290 Email: info@atitgroup.com Website: atitgroup.business.site Skype ID: ATITAcademy Youtube Channel: youtube.com/c/ATITAcademy Course Details ▪ Overview This course is for academic researchers to move with them step by step from scratch to advanced knowledge in the field of Image and Video P rocessing and Machine Learning and its related topics that allow them to be able to implement their ideas and research findings. In this course , participants will learn Python , Image Processing , and Machine Learning from scratch then, based on a systematic learning methodology, will be able to increase their knowledge to a highly - advanced level. This intensive course is the only of its type that provides complete knowledge about almost all the cutting - edge aspects of both Image Pr ocessing and Machine Learning in a practical way. This allows the participants to be able to implement any type of related research in any area E ach participant will be worked with individually to start producing a respected project Total Time Around 3 3 Hours – 9 Sessions, between 3 to 4 hours long each Workshop Sessions ▪ This comprehensi ve course will be covered over 9 sessions and contains the below topics : 1. Introduction to Image and Video Processing and Machine Learning • Introduction to Image Processing • Introduction to Video Processing • Overview of Machine Learning • Applications • Operations of Image and Video Processing • Practical Examples International ATITAcademy Int ’ l. Amman, Jor dan and Bochum, Germany WhatsApp: +962795037290. Email: info@atitgroup.com 2. Introduction to Python • Python Basics • Installing Python • PIP packages installer • Python Variables • Input and Output • If...Then...Else • Loops • Collections • Functions • Error Handling • Practical Project 3. Python for Image and Video Processing • Image Acquisition • Video Reading • Frame Enhancement • Frame Resizing • Frame as a Matrix • Frame Transformation and Sampling • Important Python Packages for Image and Video Processing ➢ OpenCV ➢ NumPy ➢ Dlip ➢ Keras ➢ Other packages • Practical Project 4. Python for Machine Learning • Supervised Learning • Unsupervised Learning • Machine Learning Algorithms and T heir Functionalities ➢ K - Nearest Neighbor algorithm (KNN) ➢ Support Vector Machine (SVM) algorithm ➢ Ensemble Learning algorithm ➢ Multi - layer Perceptron (MLP) • Deep Learning ➢ Convolutional neural networks (CNNs) • Activation Functions: ➢ RELU ➢ Sigmoid ➢ Softmax • Loss Functions: ➢ Mean Square Error ➢ Cross - Entropy Loss • Practical Project 5. Image and Video Features Extraction 1 International ATITAcademy Int ’ l. Amman, Jor dan and Bochum, Germany WhatsApp: +962795037290. Email: info@atitgroup.com • Local Features • Global Features • Edge Detection • Features Normalization • Practical Project 6. Image and Video Features Extraction 2 • Image Segmentation • K - Mean Clustering • Features Selection ➢ Principal Components Analysis (PCA) ➢ ATIT Novel Optimal Features Selection Method • Practical Project 7. Dataset Creation and Annotation ( Target Labeling ) • 2D Bounding Boxes • 3D Bounding Boxes / Cuboids • Polygons • Lines and Splines • Semantic Segmentation • Practical Project 8. Classification Problem • Supervised Learning • Image Classification in D etails • Object Recognition • Single Class vs Multi - Class • CNN in details • Classification Project 1 (General Dataset Selected by Participants ) • Classification Project 2 (Medical Dataset) 9. Regression Problem • Recurrent Neural Network (RNN) • Long Short - Term Memory (LSTM) • Object Localization • Object Tracking • Practical Project (General Dataset Selected by Participants ) ▪ A c omplete project will be assigned for participants in each session, (aside from the session - shared projects ) to work on at home, and is required to submit it at the beginning of every session starting from the 2 nd session. The submitted assignments will be discussed in the next session with each student individually ▪ Questions and discussions are highly encouraged in each session International ATITAcademy Int ’ l. Amman, Jor dan and Bochum, Germany WhatsApp: +962795037290. Email: info@atitgroup.com Remarks ▪ Each participant MUST have a suitable computer with a stable internet connection