With over five years of experience in the field, we are well-versed in the world of Artificial Intelligence, Machine Learning, and Deep Learning. Our expertise lies in both Python and JavaScript, the primary languages used to create cutting-edge AI tools. We pride ourselves on our ability to harness the power of these languages to deliver exceptional solutions for our clients.
AI and Machine Learning Services
Data Preprocessing
One of the key components in building successful AI models is data preprocessing, and we have extensive experience in this realm. We understand the criticality of data preprocessing in achieving accurate and reliable results, and we excel at implementing robust data preprocessing techniques.
Most Popular
Our repertoire includes an impressive array of models that we have developed and implemented before. From simple linear regression to complex algorithms like Convolutional Neural Networks, we have built them all. Here's a glimpse of our expertise.
Artificial Neural Networks
Building a computer system that can learn and make decisions by mimicking the structure of the human brain.
Natural Language Processing
Teaching computers to understand and process human language.
Convolutional Neural Networks
Specialized neural networks for image and pattern recognition.
Dimensionality Reduction
Reducing the number of features in a dataset while retaining important information.
Principal Component Analysis
Transforming data to find the most meaningful and informative features.
Linear Discriminant Analysis (LDA)
Reducing dimensions while maximizing class separability for classification tasks.
Kernel PCA
A variation of PCA that allows for nonlinear dimensionality reduction using kernels.
XGBoost
An advanced gradient boosting algorithm used for solving complex machine learning problems.
Additional Models
Simple Linear Regression
Finding the relationship between two variables by drawing a straight line through the data points.
Multiple Linear Regression
Similar to simple linear regression, but considering multiple variables to predict an outcome.
Polynomial Regression
Using curves instead of straight lines to fit the data points.
Support Vector Regression (SVR)
Predicting values based on a subset of training examples that serve as support vectors.
Decision Tree Regression
Making predictions by dividing the data into tree-like structures.
Random Forest Regression
Combining multiple decision trees to make accurate predictions.
Logistic Regression
Predicting the probability of an event happening using a special curve.
K-Nearest Neighbors (K-NN)
Making predictions by looking at the "neighbors" (closest data points) of the one we want to predict.
Support Vector Machine (SVM)
Creating a boundary that separates data points into different categories or groups.
Kernel SVM
Using special functions (kernels) to classify data that is not linearly separable.
Naive Bayes
Predicting outcomes by applying probabilities based on given features.
K-Means Clustering
Grouping similar data points together based on their features.
Hierarchical Clustering
Creating a tree-like structure to organize data points into clusters.
Apriori
Finding associations and patterns in data, especially in market basket analysis.
Eclat
Discovering frequent itemsets (groups of items that often appear together) in transactional data.
Reinforcement Learning/Deep Q Learning
Training a computer to make decisions by rewarding it when it takes the right actions.
Upper Confidence Bound (UCB)
A strategy that helps in finding the best action to take in a multi-armed bandit problem.
Thompson Sampling
Another approach for solving the multi-armed bandit problem by balancing exploration and exploitation.