Applied Machine Learning & Deep Learning with PyTorch

About this course

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 LSTM based sentiment analysis, RNN based spam detection, CNN models for image classification, MLP 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 PyTorchPerforming data preprocessing and feature engineering
• Training, evaluating, and deploying models with real datasets
• Understanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLPConverting research notebooks into production ready Python modules
• By the end of this course, You will be able toBuild machine learning regression & classification models
• Develop CNN, RNN, MLP, and LSTM architectures in PyTorchPerform 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

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