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Many beginners will initially rely on the train-test method to evaluate their models. This method is straightforward and seems to give a clear indication of how well a model performs on unseen data. However, this approach can often lead to an incomplete understanding of a model’s capabilities. In this blog, we’ll discuss why it’s important …
Read more “From Train-Test to Cross-Validation: Advancing Your Model’s Evaluation”
If you’re familiar with machine learning, you know that the training process allows the model to learn the optimal values for the parameters—or model coefficients—that characterize it. But machine learning models also have a set of hyperparameters whose values you should specify when training the model. So how do you find the optimal values for …
Read more “Tips for Tuning Hyperparameters in Machine Learning Models”
Statistics and Machine Learning both aim to extract insights from data, though their approaches differ significantly. Traditional statistics primarily concerns itself with inference, using the entire dataset to test hypotheses and estimate probabilities about a larger population. In contrast, machine learning emphasizes prediction and decision-making, typically employing a train-test split methodology where models learn from …
Read more “Integrating Scikit-Learn and Statsmodels for Regression”
Large Language Models (LLMs) are a hot topic right now, and everyone is getting involved in this new trend. Companies are searching for LLM engineers who can develop and implement AI solutions to optimize their workflow and reduce costs through automation, customer service, recommendations, issue resolution, and debugging. Instead of worrying that AI will take …
If you’re new to machine learning, understanding basic terms is crucial. Knowing key terms can help you understand the basics better. Here are 7 essential terms every beginner should know. These terms will give you a solid foundation to build your machine learning knowledge. 1. Algorithm An algorithm is a set of rules a computer …
Read more “7 Key Terms Every Machine Learning Beginner Should Know”
Data cleaning: whether you love it or hate it, you likely spend a lot of time doing it. It’s what we signed up for. There’s no understanding, analyzing, or modeling data without first cleaning it. Making sure we have reusable tools handy for data cleaning is essential. To that end, here are 5 DIY functions …
AutoML is a tool designed for both technical and non-technical experts. It simplifies the process of training machine learning models. All you have to do is provide it with the dataset, and in return, it will provide you with the best-performing model for your use case. You don’t have to code for long hours or …
Read more “Introduction to AutoML: Automating Machine Learning Workflows”
Machine learning presents transformative opportunities for businesses and organizations across various industries. From improving customer experiences to optimizing operations and driving innovation, the applications of machine learning are vast. However, adopting machine learning solutions is not without challenges. These challenges span across data quality, technical complexities, infrastructure requirements, and cost constraints amongst others. Understanding these …
Read more “5 Challenges in Machine Learning Adoption and How to Overcome Them”
The large Language Model, or LLM, has revolutionized how people work. By helping users generate the answer from a text prompt, LLM can do many things, such as answering questions, summarizing, planning events, and more. However, there are times when the output from LLM is not up to our standard. For example, the text generated …
Humans excel at processing vast arrays of visual information, a skill that is crucial for achieving artificial general intelligence (AGI). Over the decades, AI researchers have developed Visual Question Answering (VQA) systems to interpret scenes within single images and answer related questions. While recent advancements in foundation models have significantly closed the gap between human …
Read more “Are We Ready for Multi-Image Reasoning? Launching VHs: The Visual Haystacks Benchmark!”