What you'll learn

perform comprehensive data exploratory analysis, including data preprocessing, visualization, and feature engineering using Python

apply deep learning methods to predict global gold prices, utilizing Python and PyTorch.

evaluate the performance of deep learning models using appropriate metrics

extend acquired skills to other sequential prediction scenarios

Requirements

Familiarity with Python syntax, data types, and basic operations

Description

The global precious metal market (e.g., gold, silver, etc.) has significantly grown over the past decades and is expected to grow in future. Specifically, gold, a highly liquid asset, plays a crucial role in preventing individuals and organizations from the adverse effects of a declining dollar. Accurately predicting gold prices not only allows us to uncover evolving patterns in asset prices but also offers opportunities to make strategic investment decisions. Such knowledge is important for both investors and those new to the financial markets.This course introduce deep learning predictive analytics focusing on the global gold market. It provides detailed guidelines for analyzing and forecasting future gold prices using advanced deep learning models, such as Long Short-Term Memory (LSTM) network. Throughout the course, students will gain hands-on experience in conducting exploratory data analysis, mastering feature engineering, and building robust deep learning models using Python/PyTorch. This practical approach ensures students understand the theoretical underpinnings and apply knowledge effectively in real-world scenarios.At the end of this class, students are expected to be proficient in utilizing deep learning models (e.g., LSTM) for time series analysis and extend these applications to other domains (e.g., stock market prediction, trend analysis, temperature forecast, etc.)

Overview

Section 1: Explore and Visualize Gold Price Data

Lecture 1 Explore and Visualize Gold Price Data

Section 2: Feature Engineering

Lecture 2 Feature Engineering

Section 3: Tensor Data Preparation and LSTM Model Construction

Lecture 3 Tensor Data Preparation & Model Construction

Section 4: Predictive Model Training and Evaluation

Lecture 4 Predictive Model Training and Evaluation

Section 5: Model Performance Improvement

Lecture 5 Model Performance Improvement

python developers,people who love data science and predictive analytics