CERTIFICATES

This module introduces Scikit-Learn, an essential machine learning library in Python. It covers its core features, including supervised and unsupervised learning algorithms, model training, and evaluation. Students explore Scikit-Learn's functionalities for classification, regression, clustering, and model evaluation, establishing a foundational understanding of machine learning with this powerful library.

This module delves into practical machine learning applications using Scikit-Learn in Python. It covers supervised and unsupervised learning, including classification, regression, and clustering algorithms. Students explore model training, evaluation, and hyperparameter tuning, gaining practical insights into implementing machine learning techniques effectively with Scikit-Learn.

This module introduces TensorFlow, a popular open-source machine learning library. It covers its foundational aspects, including tensors, computation graphs, and building neural networks. Students explore TensorFlow's capabilities for creating, training, and deploying machine learning models, establishing a foundational understanding of deep learning using TensorFlow.

This module delves into the fundamentals of deep learning using TensorFlow. It covers neural network architecture, optimization, and model training techniques. Students explore TensorFlow's capabilities for building and fine-tuning deep neural networks, gaining foundational insights into implementing advanced machine learning models with TensorFlow.

This module focuses on Convolutional Neural Networks (CNNs) using TensorFlow. It covers CNN architecture, layers, and their applications in computer vision tasks. Students explore TensorFlow's CNN functionalities for image recognition, object detection, and feature extraction, gaining practical insights into implementing CNNs for various visual tasks.

Recurrent Neural Networks (RNNs) are the focus of this module, exploring their architecture and applications in sequential data analysis. Students delve into RNN fundamentals, understanding their ability to process and predict sequential data, such as text or time series. This module covers RNN variations, training techniques, and their diverse applications in natural language processing, speech recognition, and sequential data modeling.

This module introduces PyTorch, a popular deep learning framework, focusing on its fundamental components like tensors and computational graphs. Students explore PyTorch's tensor operations, computational graph creation, and manipulation, establishing a foundational understanding of PyTorch for building neural networks.

This module delves into practical deep learning applications using PyTorch. It covers building neural networks, training models, and implementing deep learning techniques. Students explore PyTorch's capabilities for creating, training, and deploying sophisticated deep learning architectures, gaining hands-on experience in implementing advanced machine learning models with PyTorch.

In this module, advanced PyTorch techniques and applications are explored. It covers topics such as customizing neural network architectures, transfer learning, fine-tuning models, and leveraging PyTorch's advanced functionalities. Students delve into complex applications, model optimization, and deploying PyTorch models for various tasks, enhancing their expertise in advanced deep learning techniques and practical applications with PyTorch.