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DSTC-A1 Online (e-LMS) Foundation

Fundamentals of Machine Learning

by - DSTC

Master Fundamentals of Machine Learning in 4 weeks through hands-on, project-based online training with DSTC.

★★★★★ Be the first to review 4 Weeks · 40 hrs e-Certificate Included
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From ₹15,000 + GST

Programme Parameters

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

About This Course

The Fundamentals of Machine Learning course is a free, beginner-friendly online self-paced course designed to introduce learners to the basic concepts of machine learning. The course explains how machines learn from data, how models are trained, and how machine learning is used in real-world applications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.

🎯 Program Aim

The Fundamentals of Machine Learning course is a free, beginner-friendly online self-paced course designed to introduce learners to the basic concepts of machine learning. The course explains how machines learn from data, how models are trained, and how machine learning is used in real-world applications.

📋 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.

👥 Who Should Enroll?

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

🚀 Key Learning Outcomes

• Tangible, reproducible Artificial Intelligence work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.

💎 What You'll Gain

🎥

Live & Recorded Sessions

Lifetime access to class recordings
🎓

e-Certificate on Completion

Cryptographically verified credential
💬

Post-Programme Support

Direct access to mentors & council
💻

Hands-On Experience

Notebooks, real-world code & datasets

Curriculum Outline

Module 1 Outline

Introduction to Machine Learning

What is Machine Learning? • Difference Between AI, ML, and Data Science • How Machines Learn from Data • Real-World Applications of Machine Learning

Module 2 Outline

Understanding Data in Machine Learning

Types of Data Used in ML • Features, Labels, and Datasets • Training Data and Testing Data • Importance of Data Quality

Module 3 Outline

Basic Machine Learning Techniques

Introduction to Supervised Learning • Regression and Classification Concepts • Introduction to Unsupervised Learning • Simple Examples of ML Use Cases

Module 4 Outline

Model Training and Evaluation

How ML Models Are Trained • Testing and Validating a Model • Accuracy and Error Concepts • Overfitting and Underfitting Basics

Module 5 Outline

Applications and Next Steps in ML

Machine Learning in Business, Healthcare, Finance, and Technology • Responsible Use of Machine Learning • Career and Learning Pathways in AI and Data Science • Mini Learning Activity / Concept-Based Practice

Technical Specifications

ParameterRequirement
Covered Tool / PlatformMachine Learning
Covered Tool / PlatformBasic Python
Covered Tool / PlatformData
Covered Tool / PlatformRegression
Covered Tool / PlatformClassification
Covered Tool / PlatformModel Evaluation

Frequently Asked Questions

Yes. This is a free online self-paced course designed for beginners who want to understand the basics of machine learning.

Students, beginners, freshers, and professionals from any background can join. No advanced technical experience is required.

You will learn basic machine learning concepts such as data, features, training, testing, prediction, regression, classification, and model evaluation.

Basic Python knowledge is helpful but not mandatory. The course focuses mainly on machine learning concepts for beginners.

Yes. Learners receive an e-Certification after successful completion of the course.

Yes. The course is designed to explain machine learning in a simple and beginner-friendly way, making it suitable for learners from technical and non-technical backgrounds.

The Fundamentals of Machine Learning course is structured as a 3-week online self-paced course.

Yes. The course introduces learners to basic regression and classification concepts as part of supervised machine learning.

Yes. This course provides a clear foundation for learners who want to continue into artificial intelligence, data science, deep learning, analytics, or advanced machine learning courses.

The course uses simple explanations, basic examples, and concept-based learning to help beginners understand how machine learning works without requiring advanced mathematics or programming knowledge. The Fundamentals of Machine Learning free course helps beginners build a clear foundation in machine learning, data-based prediction, model training, and real-world ML applications. It is a simple starting point for learners who want to explore artificial intelligence, data science, and advanced machine learning in the future.

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