Master Fundamentals of Machine Learning in 4 weeks through hands-on, project-based online training with DSTC.
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.
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.
1. Put Artificial Intelligence techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
• 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
• 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 is Machine Learning? • Difference Between AI, ML, and Data Science • How Machines Learn from Data • Real-World Applications of Machine Learning
Types of Data Used in ML • Features, Labels, and Datasets • Training Data and Testing Data • Importance of Data Quality
Introduction to Supervised Learning • Regression and Classification Concepts • Introduction to Unsupervised Learning • Simple Examples of ML Use Cases
How ML Models Are Trained • Testing and Validating a Model • Accuracy and Error Concepts • Overfitting and Underfitting Basics
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
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Machine Learning |
| Covered Tool / Platform | Basic Python |
| Covered Tool / Platform | Data |
| Covered Tool / Platform | Regression |
| Covered Tool / Platform | Classification |
| Covered Tool / Platform | Model Evaluation |
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