Industry Apps

Dive into the intriguing world of Expert Systems (ES) with this hands-on project focused on healthcare. Discover the essentials of ES, including rule-based reasoning and key components, using Pyke, a powerful Python-based knowledge engine. Learn how to leverage logic programming in the development of an ES tailored for healthcare applications. Join us as we delve into the potential, limitations, and areas for improvement of ES across healthcare and other real-world sectors.

Explore the use of Naive Bayes and scikit-learn in predicting golf play based on weather. Delve into Bayes' theorem, Laplacian correction, and the practical application of the categoricalNB() model. Uncover the significance of frequency tables, likelihood tables, and posterior probabilities in making accurate predictions.

Dive into the world of Simple Linear Regression using Scikit-Learn and Python to predict the median value of owner-occupied homes in the Boston housing dataset. Explore the dataset and its features, perform Exploratory Data Analysis (EDA), transform variables to prevent underfitting, implement Simple Linear Regression, and analyze the predictions. Understand the impact of various factors on housing prices, and learn how to create a powerful prediction model using Python and Scikit-Learn.

Delve into the world of balanced binary search trees, specifically AVL trees, and learn how to implement them using Python. Understand the fundamental differences between binary search trees and AVL trees, as well as their benefits in terms of search, insertion, and deletion operations. Examine the Node and Tree classes, the build_tree() method, and the insert() and delete() methods in detail. Explore the program implementation, testing process, and performance analysis of AVL trees in comparison to traditional binary search trees. Discover the applications of AVL trees in various data manipulation tasks and real-world scenarios.

Explore the fascinating world of word embeddings using Word2Vec, TensorFlow 2.0+, and Python to build a cutting-edge model from Wikipedia data. Dive into the intricacies of word representations, word embeddings, and the word2vec software. Examine the TensorFlow demo, its model, and dataset, while learning about the unsupervised skip-gram negative-sampling model. Understand the potential applications in natural language processing tasks such as chatbot development and learn how to create powerful word embeddings using TensorFlow and Python.

Discover the power of text dataset augmentation using round-trip translation, Python, and the NLPAug library. Learn about various text augmentation techniques, such as synonym augmentation, semantic similarity augmentation, and round-trip translation. Explore the capabilities of the NLPAug library, and see how a Python script can utilize it to augment text datasets for natural language processing tasks like chatbot training. Dive into the script’s implementation and understand the potential applications of text augmentation in improving language models.

Dive into the world of hash table techniques, focusing on linear probing and double-hashing methods, as you explore their performance characteristics, benefits, and challenges. Uncover the inner workings of linear probing and learn how rehashing can overcome its limitations. Delve into the efficiency improvements offered by double-hashing, making hash table operations more effective and versatile. Understand the practical implications and applications of these techniques, empowering you to optimize hash table usage in various scenarios.

Embark on an insightful exploration of partially and fully retroactive binary search trees (BSTs), their implementation, and performance compared to non-retroactive BSTs and simple rollback solutions. Dive into the general transformation approaches used to create retroactive BSTs, and analyze their performance under various conditions with sets of pseudo-random integers. Uncover the surprising finding that the fully retroactive implementation consistently performed slower than all other data structures, while the non-retroactive implementation emerged as the fastest.

Discover how transfer learning and the ResNet-50 network, pre-trained on the ImageNet dataset, revolutionizes flower image classification for retailers

Experience the future of customer support with a hybrid NLP chatbot, powered by Python, Flask, Seq2Seq architecture, and pretrained GloVe word embeddings for seamless technical assistance.