Artificial Intelligence is when a computer program processes data based on predetermined algorithms, analyzes patterns, and predicts relevant metrics. Through the use of Artificial Intelligence, we can optimize daily operations, analyze upcoming trends, and forecast growth patterns.

Machine Learning (ML) is a form of AI that learns how to perform a task using different learning techniques, such as learning from examples using historical data or learning by trial and error. With the implementation of machine learning, our programmers work to improve a computer’s perception of data, cognition of assigned tasks, and actions taken.

Types of ML training models:

Types of ML algorithms:

We specialize in the software below:

• Azure Machine Learning
• Amazon SageMaker
• Vertex AI
• BlueML by Explorance
• DataRobot
• Databricks
• TensorFlow

Specialization:

• We specialize in the following Areas:
• Proof Concept for AI/ML
• Vendor evaluation for AI/Ml
• Provides AI/ML,Python and RPA consultants
• Technical design and Architecture
• Development and Deployment
• Post Production Support
• Templates and Best Practices

Types of AI/ML models:

1. Linear regression: created on supervised learning, helps predict the dependent variable with the independent variable provided

• • The healthcare industry uses linear regression models to identify the risk factors of certain diseases or conditions being studied. It helps with the diagnosis of many illnesses due to the identification of illness causing symptoms.
  • Businesses use linear regression models to understand the relationship between advertising and overall revenue for certain time periods

2. Deep Neural Networks: A layered organization of neurons, each having connections to the others. Based on the received input, these neurons transmit a message or signal to other neurons, forming a complex network.

• • Image recognition, natural language processing, Speech implementation
  • Image recognition and object detection are used in Magnetic Resonance (MR) and Computed tomography (CT) processes for image segmentation, disease detection & prediction.

3. Decision Trees: a method used to find a conclusion based on past decisions, data is divided into smaller portions resembles the structure of a tree.

• • A company can use decision trees to target its advertising budget based upon collected demographic data from the past. The decision trees allow companies to analyze and forecast the best decision for their product advertising.
  • The banking industry uses decision trees to predict how likely a borrower is to have payment defaults due to their past payment history and data.

4. Logistic Regression model: a statistical model that can predict the class of the dependent variable from the set of given independent variables.

• • Logistic regression models are used in the hospitality and tourism industry to predict the user’s specified travel details and bookings available. When a user enters hypothetical trip information for inquiry, the travel sites use the logistic regression model to predict and find the best possible options for the user.

5. Support Vector machines: a quick and efficient model that excels in analyzing limited amounts of data. It is applicable to binary classification problems.

• • The face detection algorithm uses SVM to classify parts of the image as faces and non-faces and to create a square boundary around the face. This is a major part of facial recognition
  • The use of SVMs for image classification improves the accuracy of search results. As compared to traditional query-based search techniques, it provides a higher degree of accuracy.

6. Random Forest: It operates using multiple decision trees and makes the final prediction using the bagging method. The Random Forest algorithm is essentially a collection of Decision Trees. When building a decision tree, every single feature/variable in the dataset is considered, whereas when building a random forest, every single observation/row is considered, and each value of the feature/variable is averaged to provide a final decision tree.

• • The FDA uses the random forest method of machine learning to test and predict sensitivity of Drugs that are awaiting approval. The FDA uses a series of decision trees to create a random forest for the standards of examination that the drugs need to pass in order to be approved for public use

7. Learning Vector Quantization: a type of Artificial Neural Network that works on the winner-takes-all principle. It processes information by preparing a set of vectors that are then used to classify other unseen vectors.

• • This method is used heavily in the Biomedical industry for classification sampling and organization of biomedical data. It is also used in medicinal research when formulating the different medicines for various illnesses.

8. Self-organizing maps: a type of Artificial Neural Network which is also inspired by biological models of neural systems. SOM is used for clustering and mapping techniques to map multidimensional data onto lower-dimensional which allows people to reduce complex problems for easy interpretation.

• • Automotive manufacturers use SOMs to compare driving styles of users to better understand their vehicle usage and improve the designs. Individual drivers may also find this useful to analyze their vehicle usage and improve operations.

9. K-Nearest Neighbors: a simple supervised ML model used for solving both regression and classification problems. This algorithm works on the assumption that similar things (data) exist near each other and the data set is small.

• • Many streaming platforms such as Netflix, Hulu, and Amazon use KNN to find recommendations and predictions for their users on what to watch next. This is done using the KNN method because it can find what films or tv shows are most similar to the user’s preferences.