Tag: Machine Learning
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Forecasting the Future: Time Series, Prophets, and Cross-Validation
The second edition of Advanced Data Science and Analytics with Python features significant updates, focusing on trustworthy model building and practical forecasting. A chapter dedicated to Meta’s Prophet framework discusses its strengths in handling seasonality and trends, emphasising robust evaluation methods to ensure forecasts remain reliable in real-world applications.
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The Book Has Landed – 2nd Edition in Hand!
There are few moments as satisfying in a writer’s life as the thud of a package on the doorstep—especially when that package contains the culmination of years of work, revision, and relentless debugging. I’m thrilled to share that I’ve received the author copies of the second edition of Data Science and Analytics with Python, and…
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Advanced Data Science and Analytics – 2nd Edition in the making
Advanced Data Science and Analytics – 2nd Edition in the making. I have now been slowly but surely working on the revisions and, boy! there is a lot to do. I will keep you posted with the revisions.
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The 2nd Edition of Data Science and Analytics with Python is Off to Print
It’s Done! The 2nd Edition of Data Science and Analytics with Python is Off to Print! 📚🐍💻 After months of reviewing, refining, and updating, I’m thrilled to share that the final version of the 2nd Edition of Data Science and Analytics with Python has been officially submitted! 🎉 This edition is packed with fresh insights, updated techniques, and even…
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The 2nd Edition of “Data Science and Analytics with Python” is Almost Here!
I’m excited to share a major milestone in my journey as an author and educator—I’ve officially completed proofreading the 2nd Edition of Data Science and Analytics with Python! This updated edition has been a labour of love, carefully revised to incorporate the latest advancements in Python and data science techniques. From new libraries and tools to…
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Data Science and Analytics with Python – 2ed Proofs
The Journey Continues: Reviewing Proofs for the 2nd Edition of My Book “Data Science and Analytics with Python January is shaping up to be an exciting and pivotal month in my writing journey as I dive into the proofs for the 2nd edition of Data Science and Analytics with Python. It’s a moment of both pride…
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Standards in AI — The new AI Standards Hub
The advances of systems powered by machine learning and artificial intelligence are undeniable and at times pretty cool. Think of how the recent announcement of ChatGPT has captured the collective imagination of over a million users in 6 days. There are other applications that do not have the same level of coverage but which may carry out…
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Diffusion Models – More than adding noise
I am sure you may have come across a few examples of the prowess of DALL-E and Stable diffusion. In my most recent post in the Domino Data Blog I explore diffusion probabilistic machine learning models by talking about jackalopes and the sugar in a cup of coffee. Take a look!
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Building Robust Models with Cross-Validation
So, you have a machine learning model! Congratulations! Now what? Well, as I always remind colleagues, there is no such thing as a perfect model, only good enough ones. Ensuring that your machine learning model is robust and can generalise well is an important task. This is where cross-validation can help. Take a look at…
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Transformers – Self-Attention to the rescue
If the mention of “Transformers” brings to mind the adventures of autonomous robots in disguise you are probably, like me, a child of the 80s. However, a different kind of transformers is what a lot of people in Machine Learning are playing with. In my most recent post in the Domino Data Lab blog I cover what transformers…