A Window to the Tech World is a weekly podcast hosted by Dr Somdip Dey (InteliDey)—an embedded/on-device AI scientist, MIT Innovator Under 35 (Europe, AI & Robotics), Forbes-named among 20 successful tech founders, and a Life Fellow of the Royal Society of Arts. 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. Clear explanations, real-world examples, occasional guests—no hype, just signal.
A Window to the Tech World
Claim This Podcastby Somdip Dey (InteliDey)
7 episodes
Updated Daily
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Podcast Overview
A Window to the Tech World is a weekly podcast hosted by Dr Somdip Dey (InteliDey)—an embedded/on-device AI scientist, MIT Innovator Under 35 (Europe, AI & Robotics), Forbes-named among 20 successful tech founders, and a Life Fellow of the Royal Society of Arts. 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. Clear explanations, real-world examples, occasional guests—no hype, just signal.
Language
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Publishing Since
2/13/2026
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Recent Episodes
February 19, 2026
ML 101: Where Do Decision Trees & Random Forests Fit in Machine Learning Types?
<p>After learning the main types of machine learning, this short <strong>Machine Learning 101</strong> episode answers a practical question: <strong>where do Decision Trees and Random Forests fit?</strong> We explain why these models are most commonly used for <strong>supervised learning</strong>—both <strong>classification</strong> (spam vs not spam, fraud vs not fraud) and <strong>regression</strong>(house prices, delivery time). We also touch on how tree-based methods can be adapted for <strong>unsupervised</strong> tasks like anomaly detection, but why their standard form is supervised. Clear real-world examples included.</p>
February 19, 2026
ML 101: Types of Machine Learning — Supervised, Unsupervised, Semi-Supervised & Reinforcement
<p>In this <strong>Machine Learning 101</strong> episode, we explain the <strong>four main types of machine learning</strong>—<strong>Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning</strong>—in plain English with real-world examples. We start from the basics (what <strong>features</strong>, <strong>labels</strong>, and <strong>classes</strong> 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 <strong>house-price prediction, spam/fraud detection, customer segmentation, medical imaging with limited labels, and reward-based learning in robotics and games</strong>—plus common pitfalls like bias, privacy, and data leakage.</p>
February 15, 2026
ML 101: Ensemble Modelling — Random Forests & Gradient Boosted Trees
<p>In this <strong>Machine Learning 101</strong> episode, we explain <strong>ensemble modelling</strong>—how combining multiple models can create one powerful predictor. You’ll learn the difference between <strong>bagging</strong> and <strong>boosting</strong>, then dive into two of the most popular tree-based ensembles: <strong>Random Forests</strong> (many “randomised” decision trees voting/averaging together to reduce overfitting) and <strong>Gradient Boosted Trees</strong> (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 <strong>OOB error</strong>. We finish with evaluation tips, common pitfalls, and a quick note on bias and responsible use.</p>
7 total episodes available
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- What is A Window to the Tech World?
- How often does this podcast release new episodes?
This podcast updates daily.
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This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
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Information about guest appearances is not available.
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