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Explore Object-Oriented Programming (OOP) in Python, its core concepts, and key differences from Procedure-Oriented Programming.
Looking for datasets for deep learning? Explore our list of openly available datasets that can help you master image processing, speech recognition, and more.
Explore the decision tree algorithm and how it simplifies classification and regression tasks in machine learning. Read Now!
Explore the architecture, training, and prediction processes of 12 types of neural networks in deep learning, including CNNs, LSTMs, and RNNs
Learn how to tackle class imbalance in machine learning. Explore techniques, examples, and methodologies to improve model performance!
Learn why models lose stability & explore various cross validation methods, including k-fold and LOOCV, to measure bias variance effectively.
Implement pre-trained models for image classification (VGG-16, Inception, ResNet50, EfficientNet) with data augmentation and model training.
Learn the fundamentals of Support Vector Machine (SVM) and its applications in classification and regression. Understand about SVM in machine learning.
Word embeddings are techniques used in natural language processing. This includes tools & techniques like word2vec, TD-IDF, count vectors, etc. Explore Now!
Learn about Natural Language Processing (NLP) and why it matters. Dive into text prep, key tasks, and top Python tools for NLP. Start Reading Now!
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