After learning the main types of machine learning, this short Machine Learning 101 episode answers a practical question: where do Decision Trees and Random Forests fit? We explain why these models are most commonly used for supervised learning—both classification (spam vs not spam, fraud vs not fraud) and regression(house prices, delivery time). We also touch on how tree-based methods can be adapted for unsupervised tasks like anomaly detection, but why their standard form is supervised. Clear real-world examples included.

In this Machine Learning 101 episode, we explain the four main types of machine learning—Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning—in plain English with real-world examples. We start from the basics (what features, labels, and classes mean), then explore when each learning type is used, its advantages and disadvantages, and how to choose the right approach in practice. You’ll hear relatable examples like house-price prediction, spam/fraud detection, customer segmentation, medical imaging with limited labels, and reward-based learning in robotics and games—plus common pitfalls like bias, privacy, and data leakage.

In this Machine Learning 101 episode, we explain ensemble modelling—how combining multiple models can create one powerful predictor. You’ll learn the difference between bagging and boosting, then dive into two of the most popular tree-based ensembles: Random Forests (many “randomised” decision trees voting/averaging together to reduce overfitting) and Gradient Boosted Trees (trees built sequentially, each correcting the last model’s mistakes). We use simple, real-world examples, then add an advanced section on key concepts such as OOB error. We finish with evaluation tips, common pitfalls, and a quick note on bias and responsible use.