Machine Learning
No data analytics service offering would be complete without Machine Learning (ML) expertise. Data, both big and small, has proven to be amendable to machine learning techniques and these techniques have moved from the research laboratory to accessibility everywhere through APIs provided (some for free) from the likes of Microsoft, Amazon and Google.
Some of the most highly publicized forms of these ML techniques, such as image recognition, or sentiment analysis are a mere mouse-click away. Others, like feature engineering take a bit of know-how and time to implement correctly. For example, as data scientists, we often rely on model parameters to tune a model’s sensitivity or accuracy and often this process can require extensive trial and error to find the right settings. But with ML techniques such as parameter sweeping, entire sets of parameters can be tried in parallel and distributed across a cloud’s compute nodes. Using techniques like these, we can often find boosts in modelling accuracy that would otherwise be elusive and time-consuming to locate.
Often, linear classification logarithms can be fast tracked as some classification techniques yield to linearity (or a higher dimensional analog). Sometimes, this can lead to spectacularly inaccurate results:
NanoLogic’s track record in data analytics has given us the wisdom and experience to know which algorithm’s are best for which tasks. We are aware of the pitfalls that all data science entails and we take active steps to ensure we don’t walk into them inadvertently. Whether it’s supervised, unsupervised or reinforcement based learning techniques, NanoLogic can help you leverage ML in your data analytics. Find out how.