PyTorch for Business Forecasting & Predictive Analytics

Course Description

This tutorial course provides a practical, business oriented introduction to deep learning using PyTorch. You will learn how to build predictive models that solve real industry problems, such as forecasting inventory demand using LSTM, predicting customer lifetime value, identifying cross sell opportunities with ANN, detecting sales anomalies, measuring promotion performance using MLP, and comparing regional sales with DNN models.

Throughout this tutorial course, you will work hands on with real datasets, learn how to preprocess business data, design neural network architectures, train models step by step, evaluate accuracy, and interpret results for business decision-making. This tutorial course is designed to help professionals move from simple ML concepts to fully functional deep learning models used in retail, ERP, Finance, Supply chain, and CRM analytics.

This Tutorial course primarily focuses on Applying deep learning techniques to solve real world business forecasting problems such as demand prediction, sales analysis, customer behavior forecasting, and promotional performance measurement.

By the end of this tutorial course, you will be able to

Build LSTM models for demand, ERP sales, and anomaly detection

Use ANN, MLP, DNN for cross sell, CLV, promotional success & regional sales analysis

Convert business datasets into deep learning training pipelines

Evaluate and deploy business prediction models in PyTorch

In this tutorial course, You will learn, how to build end to end predictive analytics models such as

Inventory demand predictor using LSTM

CLV forecaster using PyTorch

ANN model for cross selling & repeat customer prediction

LSTM model for sales trend & anomaly detection

MLP & DNN for promotional success & regional performance scoring