Applied Machine Learning & Deep Learning with PyTorch

Course Description This tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTMs based sentiment analysis, RNNs based spam detection, CNNs models for image classification, MLPs networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML & DL models end to end in PyTorch Performing data preprocessing and feature engineering Training, evaluating, and deploying models with real datasets Understanding architectures like LSTMs, CNNs, DNNs, Decision Trees, Random Forest & MLPs Converting research notebooks into production ready Python modules By the end of this course, You will be able to Build machine learning regression & classification models Develop CNNs, RNNs, MLPs, and LSTMs architectures in PyTorch Perform NLP tasks like sentiment analysis & spam detection Implement image classification models for handwritten alphabets & traffic signs Convert notebooks into modular Python project structures Work with real time data for prediction and quality assessment You will learn in this tutorial course Dec