Machine Learning Class 2 | Types of ML, Features vs Labels & Problem Framing | STeach Academy

Welcome to Machine Learning – Class 2 at STeach Academy! In this lecture, we'll explore the three major types of Machine Learning and understand how to identify features, labels, and targets in a dataset. We'll also learn how to frame a real-world problem as a Machine Learning task using simple, practical examples. This lecture is beginner-friendly and builds on the concepts covered in Class 1. Topics Covered: Quick Recap of Machine Learning – Class 1 Supervised Learning Classification vs. Regression Unsupervised Learning and Clustering Reinforcement Learning Features vs. Labels (X and y) Understanding Datasets with Practical Examples Problem Framing: Converting Real-World Problems into ML Problems Identifying Data, Features, Targets, and ML Task Types Interactive Quiz and Concept Recap PPT and Class Materials: Access the lecture slides and learning materials through the Google Drive link below: https://drive.google.com/drive/folder... Homework: Choose any two real-world Machine Learning applications. For each application, identify: 1. What problem is being solved? 2. Which type of Machine Learning could be used? 3. What data would be required? 4. What are the possible features? 5. What is the target or label, if applicable? 6. Is it a classification or regression task, if applicable? No Python coding is required for this activity. Focus on understanding the problem and organizing your ideas. What's Next? In our upcoming classes, we'll continue building our Machine Learning foundation and gradually move towards preparing real-world datasets and implementing ML algorithms using Python. If you missed the live class, watch this lecture to catch up. If you attended, use it to revise the concepts and strengthen your understanding. Subscribe to STeach Academy for more beginner-friendly programming and Machine Learning lessons. STeach Academy — I learn. I teach. We grow. #MachineLearning #MachineLearningForBeginners #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #Python #STeachAcademy