Image by AuthorA Practical Guide to Deploying Machine Learning Models As a data scientist, you probably know how to build machine learning models. But it’s only when you deploy the model that you get a useful machine learning solution. And if you’re looking to learn more about deploying machine learning models, this guide is for you. The steps involved in building and deploying ML models can typically be summed up like so: building the model, creating an API to serve […]

Understanding RAG Part I: Why It’s NeededImage by Editor | Midjourney Natural language processing (NLP) is an area of artificial intelligence (AI) aimed at teaching computers to understand written and verbal human language and interact with humans by using such a language. Whilst traditional NLP methods have been studied for decades, the recent emergence of large language models (LLMs) has virtually taken over all developments in the field. By combining sophisticated deep learning architectures with the self-attention mechanism capable of […]

The Complete Guide to Feature Engineering for Better Model Performance Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your data. They help the model understand and make predictions. Feature engineering improves these features to make them more useful. Modeling […]

5 Real-World Machine Learning Projects You Can Build This WeekendImage by Author | Created on Canva Building machine learning projects using real-world datasets is an effective way to apply what you’ve learned. Working with real-world datasets will help you learn a great deal about cleaning and analyzing messy data, handling class imbalance, and much more. But to build truly helpful machine learning models, it’s also important to go beyond training and evaluating models and build APIs and dashboards as needed. […]

Comparing Scikit-Learn and TensorFlow for Machine LearningImage by Editor | Ideogram Choosing a machine learning (ML) library to learn and utilize is essential during the journey of mastering this enthralling discipline of AI. Understanding the strengths and limitations of popular libraries like Scikit-learn and TensorFlow is essential to choose the one that adapts to your needs. This article discusses and compares these two popular Python libraries for ML under eight criteria. Scope of Models and Techniques Let’s start by highlighting […]

Recommender systems may be the most common type of predictive model that the average person may encounter. They provide the basis for recommendations on services such as Amazon, Spotify, and Youtube. Recommender systems are a huge daunting topic if you’re just getting started. There is a myriad of data preparation techniques, algorithms, and model evaluation methods. Not all of the techniques will be relevant, and in fact, the state-of-the-art can be ignored for now as you will likely get very […]

Last Updated on August 16, 2020 This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonized over what to include and what to exclude. I had to work hard to put myself in the […]

Last Updated on August 16, 2020 When I’m asked about resources for big data, I typically recommend people watch Peter Norvig’s Big Data tech talk to Facebook Engineering from 2009. It’s fantastic because he’s a great communicator and clearly and presents the deceptively simple thesis of big data in this video. In this blog post I summarize this video for you into cliff notes you can review. Essentially, all models are wrong, but some are useful. Quote by George Box. […]

Last Updated on August 16, 2020 There are a lot of good books on machine learning, but most people buy the wrong ones. A question I get asked the most is what books should people buy to get stared in machine learning. My answer to beginners is: “don’t buy textbooks“. In this post I want to point out a few key books that are aimed at beginners that you should buy (and read!) if you are just starting out. I […]

Last Updated on September 29, 2016 There are a lot of machine learning books and courses available and a trend towards free university courses and ebooks. With so much excellent resources available it can feel overwhelming. So much so that it may prevent you from getting started or making progress. In this post I want to share with you my tips for self study that allow me to touch a resource once, extract everything I think I can learn from […]