Hands on Deep Learning Projects Stock Price Prognostics teaches how neural networks analyse market data, identify patterns and forecast stock price movements through practical projects, helping learners understand predictive modelling.
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Intermediate
33 Students 
1 hour, 51 minutes 
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Enroll in the Hands on Deep Learning Projects - Stock price Prognostics course to master Deep Learning Models for Stock Prediction using advanced Time Series Analysis. This course focuses on LSTM Networks, Market Trends, and Price Forecasting while building strong foundations in Financial Analytics. Learn Data Preprocessing and Feature Engineering for real-world trading insights.
The curriculum starts with Introduction, Installation of Tools and Libraries, and Dataset exploration. You will perform EDA, Feature Scaling, and apply Data Preprocessing techniques. The course then advances to building RNN and LSTM Networks for Stock Prediction, analyzing Market Trends, and improving Model Evaluation for accurate Price Forecasting results insights.
By the end of this course, you will build real-world Deep Learning Models for Stock Prediction using Time Series Analysis and LSTM Networks. You will generate accurate Price Forecasting outputs, evaluate Model Evaluation metrics, and understand Market Trends. This hands-on project strengthens Financial Analytics, Feature Engineering, and Data Preprocessing skills.
After completing the Hands on Deep Learning Projects - Stock price Prognostics course assessment, you will be eligible to receive a CPD-accredited certificate worth £9 from One Education to demonstrate your achievement.
The certificate is also available as a printed hard copy delivered by post for £15.
Why Study This Hands on Deep Learning Projects - Stock Price Prognostics Course? The growing use of artificial intelligence in financial markets has increased demand for professionals skilled in predictive modelling and data-driven stock analysis to support informed decision-making.
Studying this course develops practical skills in deep learning techniques, time-series forecasting, and model development using modern AI frameworks. It enhances analytical capability and prepares learners for roles in financial data science and quantitative research.
The Hands on Deep Learning Projects - Stock Price Prognostics Course has a total study time of 1 hour, 51 minutes. Learners can complete the programme at a flexible, self-paced schedule, allowing them to develop practical understanding of deep learning techniques applied to stock price prediction and forecasting in a way that fits comfortably around their availability and learning needs.
The Hands on Deep Learning Projects - Stock Price Prognostics course requirements are suitable for learners interested in applying artificial intelligence to financial forecasting. Applicants should have basic programming knowledge, an understanding of machine learning concepts, and familiarity with mathematics such as statistics and linear algebra. Access to an internet-enabled device is required for flexible, self-paced online learning.
It is a practical deep learning course focused on building real-world projects to predict and analyse stock price movements using machine learning and time-series forecasting techniques.
It is suitable for data science students, AI enthusiasts, software developers, finance professionals, and anyone interested in applying deep learning to financial markets.
You will learn about neural networks, LSTM models, time-series analysis, data preprocessing, feature engineering, model training, and evaluating stock price prediction models.
Yes, basic knowledge of Python, machine learning, and statistics is recommended before starting this course.
It helps in financial decision-making, investment planning, risk analysis, and understanding market trends using data-driven methods.
No, it provides educational tools and models, but financial markets are highly volatile and predictions are not guaranteed.
| Section 01: Introduction | |||
| Introduction of Project | 00:04:00 | ||
| Section 02: Installation of Tools and Libraries | |||
| Installation | 00:06:00 | ||
| Libraries | 00:11:00 | ||
| Section 03: Dataset | |||
| Dataset Explore | 00:05:00 | ||
| Import Libraries | 00:05:00 | ||
| Data Preprocessing | 00:06:00 | ||
| Section 04: EDA | |||
| Exploratory Data Analysis | 00:08:00 | ||
| Exploratory Data Analysis Continue | 00:07:00 | ||
| Section 05: Feature Scaling | |||
| Feature Scaling | 00:08:00 | ||
| Feature Scaling Continue | 00:07:00 | ||
| More on Feature Scaling | 00:05:00 | ||
| Section 06: Building RNN | |||
| Building RNN | 00:08:00 | ||
| Building RNN Continue | 00:07:00 | ||
| Training of Network | 00:05:00 | ||
| Section 07: Prediction on Test Data | |||
| Prediction on Test Data | 00:07:00 | ||
| Prediction on Test Data Continue | 00:07:00 | ||
| Section 08: Output | |||
| Final Result Visualization | 00:05:00 | ||
| Order Your Certificate | |||
| Order Your Certificate Now | 00:00:00 | ||
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