To build and train a simple encoder–decoder RNN model that can learn to translate short sentences from a source language to a target language, demonstrating the basic idea of sequence‑to‑sequence neural machine translation

To develop and train a simple Recurrent Neural Network (RNN) model that can learn patterns from text data and accurately predict the next word in a sentence, demonstrating the basic concepts of sequence modeling and neural language prediction.

Simple Python program using RNN to predict the next word in a sequence

To implement a simple RNN-based sequence-to-sequence model that translates short English sentences into French.

Python program to build a machine learning model which will predict whether or not it will rain tomorrow by studying past data.

To perform and understand key image preprocessing steps, including RGB conversion, normalization, and augmentation, and to compare these results by plotting the original, normalized, and augmented images in a single figure.

To build and evaluate a Convolutional Neural Network (CNN) for handwritten digit image classification. The objective is to load the dataset, preprocess image inputs, design a CNN architecture, train the model, and measure its performance using accuracy and loss metrics.

To apply various image preprocessing operations on a digital image using Python in Google Colab. The objective is to understand and implement transformations such as mirroring, scaling, rotation, grayscale conversion, resizing, and additional enhancement methods like blurring, edge detection, brightness and contrast adjustment, sharpening, noise addition, and cropping.

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