Applied Machine Learning & Deep Learning With Pytorch

What you'll learn
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
Requirements
Basic Python knowledge
Understanding of machine learning concepts
No prior PyTorch experience required
No prior deep learning experience required
Very basic maths
Description
Course DescriptionThis 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 engineeringTraining, evaluating, and deploying models with real datasetsUnderstanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLPConverting research notebooks into production ready Python modulesBy the end of this course, You will be able toBuild machine learning regression & classification modelsDevelop CNN, RNN, MLP, and LSTM architectures in PyTorchPerform NLP tasks like sentiment analysis & spam detectionImplement image classification models for handwritten alphabets & traffic signsConvert notebooks into modular Python project structuresWork with real time data for prediction and quality assessmentYou will learn in this tutorial courseDecision Tree & Random Forest RegressionLinear Regression with practical datasetsLSTM based sentiment analysisRNN based spam classificationCNN for alphabets & traffic sign recognitionMLP for video quality predictionDNN for product quality assessmentProfessional PyTorch project structuring
Anyone wanting to learn PyTorch,Professionals who want strong conceptual & practical deep learning understanding,Professionals preparing for ML & DL interviews,Developers wanting to build deep learning projects,Beginners stepping into Machine Learning,Data analysts transitioning to ML,Professionals preparing for AI & ML job roles,Anyone who wants project based ML skills
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