After completing a course and mastering the essentials of machine learning, it is time to start building machine learning models using real-world datasets. While accessing most real-world datasets can be expensive, platforms like Kaggle offer access to the latest datasets for free on a wide variety of machine learning tasks. In this blog, we will […]
Collaborating on a machine learning project is a bit different from collaborating on a traditional software project. In a machine learning project, engineers are working with data, models, and source code. Additionally, they are also sharing features, model experiment results, and pipelines. You can’t just use any code-sharing platform for a machine learning project; you […]
A loss function in machine learning is a mathematical formula that calculates the difference between the predicted output and the actual output of the model. The loss function is then used to slightly change the model weights and then check whether it has improved the model’s performance. The goal of machine learning algorithms is to […]
If you’re reading this article, I assume you already know what machine learning is. But just for a quick refresher, it’s simply making computers smart enough to do jobs that humans used to do, for example, taking attendance using facial recognition. Anyway, moving on to our main discussion, I know there are a lot of […]
Introduction The process of deploying machine learning models is an important part of deploying AI technologies and systems to the real world. Unfortunately, the road to model deployment can be a tough one. The process of deployment is often characterized by challenges associated with taking a trained model — the culmination of a lengthy data-preparation […]
Introduction Imperfect data is the norm rather than the exception in machine learning. Comparably common is the binary class imbalance when the classes in a trained data remains majority/minority class, or is moderately skewed. Imbalanced data can undermine a machine learning model by producing model selection biases. Therefore in the interest of model performance and […]
Introduction Classification algorithms are at the heart of data science, helping us categorize and organize data into pre-defined classes. These algorithms are used in a wide array of applications, from spam detection and medical diagnosis to image recognition and customer profiling. It is for this reason that those new to data science must know about […]
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 […]
The Complete Machine Learning Bookshelf. Books are a fantastic investment. You get years of experience for tens of dollars. I love books and I read every machine learning book I can get my hands on. I think having good references is the fastest way to getting good answers to your machine learning questions, and having […]
Machine learning podcasts are now a thing. There are now enough of us interested in this obscure geeky topic that there are podcasts dedicated to chatting about the ins and outs of predictive modeling. There has never been a better time to get started and working in this amazing field. In this post, I want […]
