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 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 PyTorch

Performing data preprocessing and feature engineering

Training, evaluating, and deploying models with real datasets

Understanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLP

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 CNN, RNN, MLP, and LSTM 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

Decision Tree & Random Forest Regression

Linear Regression with practical datasets

LSTM based sentiment analysis

RNN based spam classification

CNN for alphabets & traffic sign recognition

MLP for video quality prediction

DNN for product quality assessment

Professional PyTorch project structuring