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DSTC-A13 Online (e-LMS) Foundation

Machine Learning in Finance: Basics

by - DSTC

Master Machine Learning in Finance: Basics in 4 weeks through hands-on, project-based online training with DSTC.

β˜…β˜…β˜…β˜…β˜… Be the first to review β€’ 4 Weeks Β· 40 hrs β€’ e-Certificate Included
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From β‚Ή200 + GST

Programme Parameters

Educational Level:
Foundation
Duration & Workload:
4 Weeks (40 Hrs)
Delivery Mode:
Online (e-LMS)
Prerequisites:
β€’ No prior experience required β€” basic computer literacy is sufficient.
β€’ A laptop or desktop with a stable internet connection.
β€’ Willingness to complete assignments and the capstone project.

About This Course

The Machine Learning in Finance: Basics course is a free, beginner-friendly self-paced program designed to introduce learners to how machine learning is applied in the financial domain. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.

🎯 Program Aim

The Machine Learning in Finance: Basics course is a free, beginner-friendly self-paced program designed to introduce learners to how machine learning is applied in the financial domain.

πŸ“‹ Course Objectives

1. Put Artificial Intelligence techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.

πŸ‘₯ Who Should Enroll?

β€’ Master's and senior undergraduate students specializing in Artificial Intelligence
β€’ R&D engineers and working professionals applying Artificial Intelligence in industry
β€’ Academics and educators building research or teaching capacity in Artificial Intelligence

πŸš€ Key Learning Outcomes

β€’ A portfolio-grade Artificial Intelligence deliverable you can defend and extend.
β€’ A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.

πŸ’Ž What You'll Gain

πŸŽ₯

Live & Recorded Sessions

Lifetime access to class recordings
πŸŽ“

e-Certificate on Completion

Cryptographically verified credential
πŸ’¬

Post-Programme Support

Direct access to mentors & council
πŸ’»

Hands-On Experience

Notebooks, real-world code & datasets

Curriculum Outline

Module 1 Outline

Introduction to Machine Learning in Finance

What is Machine Learning in Finance? β€’ Role of Data in Financial Systems β€’ Applications of ML in Banking and Investment β€’ Overview of Financial Decision-Making

Module 2 Outline

Understanding Financial Data

Types of Financial Data: Market, Transactions, and Time Series β€’ Introduction to Stock, Price, and Market Trends β€’ Features and Variables in Financial Analysis β€’ Importance of Data Quality

Module 3 Outline

Basic ML Applications in Finance

Predicting Prices and Trends β€’ Credit Scoring and Risk Assessment β€’ Fraud Detection Concepts β€’ Customer Analytics in Financial Services

Module 4 Outline

Model Evaluation and Risk Basics

Understanding Prediction Accuracy β€’ Risk and Uncertainty in Financial Models β€’ Overfitting and Reliability Basics β€’ Interpreting Model Results in Finance

Module 5 Outline

Applications and Future Scope

AI in Investment, Trading, and Banking β€’ Role of ML in FinTech and Digital Finance β€’ Career Opportunities in AI and Finance β€’ Mini Learning Activity / Concept-Based Practice

Technical Specifications

ParameterRequirement
Covered Tool / PlatformMachine Learning
Covered Tool / PlatformFinancial Data
Covered Tool / PlatformPredictive Analytics
Covered Tool / PlatformRisk Analysis
Covered Tool / PlatformData Trends

Frequently Asked Questions

Yes. This is a free online self-paced course designed for beginners.

No. The course focuses on basic concepts and is suitable for learners from non-technical and non-finance backgrounds.

You will learn how machine learning is used in finance, including prediction, risk analysis, fraud detection, and financial decision-making.

Students, beginners, and professionals from finance, business, engineering, and other fields can join.

Yes. Learners receive an e-Certification after completing the course.

Machine learning in finance refers to using data-driven models to analyze financial information, predict trends, manage risk, detect fraud, and support better financial decisions.

Yes. The course is suitable for learners from commerce, management, finance, economics, business, engineering, and other data-related backgrounds.

The Machine Learning in Finance: Basics course is designed as a 2–3 week online self-paced course.

Yes. The course introduces fraud detection concepts, credit scoring, risk assessment, prediction, and reliability basics in financial models.

The course explains financial data, prediction models, risk analysis, fraud detection, and finance use cases in simple language without requiring prior coding, finance, or machine learning knowledge. The Machine Learning in Finance: Basics course provides a simple and structured introduction to how AI and machine learning are transforming financial systems. It helps learners understand financial data, prediction models, and real-world applications, making it an ideal starting point for exploring fintech, analytics, and AI-driven finance.

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