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.

In this Machine Learning 101 episode, we break down Decision Trees—one of the most intuitive ML models—using simple, everyday examples you can picture like a flowchart. You’ll learn how trees make decisions step by step, how they handle classification (yes/no choices like “take an umbrella?”) and regression (predicting numbers like delivery time), and why trees can overfit if they grow too deep. For more advanced listeners, we cover how splits are chosen (e.g., Gini vs entropy/information gain, and MSE reduction for regression trees). We finish with evaluation basics and a quick note on bias and responsible use.

In this Machine Learning 101 episode, we explore regression—one of the simplest and most practical ML techniques—using clear, non-technical examples. We cover Linear Regression (predicting numbers like house prices), Logistic Regression (yes/no decisions using probability, like spam or fraud), Multivariate/Multiple Regression (using many factors at once), and Polynomial Regression (capturing curved relationships like diminishing returns). You’ll also learn how to choose the right approach, how to evaluate results, and a quick note on bias and responsible use.

In this episode of Machine Learning 101, we untangle the terms people use interchangeably: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI). You’ll learn the simple “nested circles” hierarchy, see everyday examples, and understand where tools like ChatGPT and Google Gemini fit (GenAI apps powered by large language models). We also cover a quick reality check on bias, hallucinations, and privacy, plus what this means if you’re starting or switching into a tech career.

A Window to the Tech World is a weekly podcast hosted by Dr Somdip Dey (InteliDey), an embedded/on-device AI scientist and tech columnist. Each episode breaks down the biggest shifts in AI, cybersecurity, data and digital innovation—then turns them into practical career guidance: what to learn, what to build, how to interview, and how to grow in tech responsibly. Expect clear explanations, real-world examples, and occasional guest insights—no hype, just signal.