Expertise / AI Application Development

AI Application Development

MACHINE LEARNING CHALLENGES

Much of real-world data is unstructured – images, video, audio, text, etc. With the abundance of data science tools on the market today, it’s possible to make sense of this data by training machines – software – to interpret, categorize, and identify meaningful patterns and trends.

Tools help, but they only get us so far. What makes the difference is having a fundamental knowledge of the concepts that underpin the tools and being able to refine and optimize approaches to generate the desired result.

MACHINE LEARNING USE CASES

From narrow AI to petabytes of noise

Machine Learning does not automatically mean Artificial General Intelligence (AGI) or massive data models like GPT-3. In most cases, it is the more practical and ‘modest’ goals – anomaly detection, image recognition, predictive analytics, and optimization models – that currently deliver the highest returns on investment to businesses. With the wealth of data available to us today, the possibilities are almost endless but turning possibility into reality requires an experienced, expert data science team. That’s where Cledar comes in.

Let’s talk about how you can harness the power of Machine Learning for your business.

Meet Joanna from our Data Science team

MACHINE LEARNING SERVICES

We turn Machine Learning R&D into business results

Some examples of the work we do:

Day-to-Day Toolkit

Python, SciPy, NumPy, Scikit-learn, PyTorch, Pandas, XGBoost, Tensorflow, Keras

Clustering

k-means, k-nearest neighbors (k-NN), Mean-Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), EM using GMM, Hierarchical Clustering (HAC)

Dimensionality Reduction

Principle Component Analysis (PCA), Independent Component Analysis (ICA), Singular Value Decomposition (SVD), k-d trees

Other Concepts

Entropy, Regression, multivariate regression, Deep learning, Convolutional Neural Networks (CNN), Singular Spectrum Analysis

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