Artificial Intelligence And Machine Learning
Supervised Learning
This method relies on the training dataset to learn functions from inputs and meet the desired output values through methods like regression, classification & prediction. Multiple iterations ensure efficient mapping and accurate predictions of business outcomes. Yield superior results from our guided learning models, from spam filtering to improved products, meaningful insights, quick decision-making, risk analysis, and more.
Unsupervised Learning
Develop future-ready applications across different business cases that learn and adapt over time with usage by building models that explore, assess and process unstructured data and find some structure and insights within. Uncover hidden relationships, classify customer needs, target marketing campaigns, enable text understanding, and much more to reshape the operations of your business world.
Reinforcement Learning
The reinforcement learning model focuses on determining actions that can optimize performance and yield the best reward over time. This technique uses experimentative training to figure out how to achieve optimal results in a given environment and stay ahead of disruption. Its dynamic applications span the fields of navigation, robotics, gaming, telecommunications, and more.
Deep Learning: Deep learning is the bedrock of high-level synthetic intelligence. While machine learning focuses on available data and known properties, deep learning uses a layered approach of artificial neural networks to discover scalable solutions through predictive and prescriptive analysis. The model essentially learns, interacts, and performs complex tasks without human intervention
Our Process
Is your business ready for machine learning yet? Let’s find out! Our machine learning consultants help you identify business challenges to resolve and find functional solutions by following the 7 step approach to implementing machine learning solutions.
- Identify Problem and Collect Data
- Prepare Data for Analysis
- Transform the Data
- Data Splitting
- Create Models
- Test and Validate Models
- Deploy Models
1.Identify Problem and Collect Data:
Our team of consultants and data scientists take on the preliminary work of evaluating your business objectives and determining the relevant solutions to the problems that are posed. Based on the outlined goals, qualitative and quantitative data is extracted for analysis.
2.Prepare Data for Analysis:
Raw data requires a lot of pre processing to make it usable and efficient. We clean, normalize, label, classify the collected data and eliminate the unusable parts. Pertinent visualizations are prepared to examine its scope and uncover hidden connections.
3.Transform the Data:
This is the consolidation stage of data processing, where the data is transformed into forms appropriate for mining and getting intelligent insights. The data is simplified by normalization, attribute decomposition and aggregated into understandable categories to make it uniform.
4.Data Splitting:
Data splitting focuses on 3 main subsets: training, testing, and validation. Training data is a learning sample for the model, test data ensures performance improvement, and validation data equips the model for unforeseen tasks. This process builds a robust and reliable model.
5.Create Models:
At this stage, the transformed training data is used to create multiple algorithm models. Depending on the desired outcomes of the task at hand, supervised or unsupervised learning method is applied for experimentative analysis using set parameters.
6.Test and Validate Models:
The created models are now put to the test to check for the best results. Cross-validation and ensembling techniques are used to scale speed, accuracy, efficiency, and performance. The goal is to tune the algorithm and develop a successfully optimized model.
7.Deploy Models:
By this stage, we have a production-grade model ready for deployment. For optimum performance and smooth integration, A/B testing and modifications are implemented. The model is now ready to make inferences.
2.Computer Vision:
Rapid advances in Artificial Intelligence have enabled programs to process countless digital images and videos. With considerable amount of digital data being generated these days in the form of text, audio, video, and images, organizations must equip themselves competently to address the evolving demands of analytics-driven by this change.
We integrate computer vision services as well as train models to identify specific places, people, and objects and categorize them to retrieve valuable information as well as analytics.
Image Segmentation:This process involves segmenting an image into multiple homogeneous regions based on certain similarity parameters, so that each region can be individually analyzed and is different from its neighboring regions. Such categorization helps in tagging people, labeling objects, face recognition, traffic control, and various other tasks.
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Contextual Image ClassificationHumans can separate a person or object from their surroundings by identifying boundaries and doing a comparative check with memories or records of similar entities. Computers require a certain context to classify things and form a relationship between pixelated regions. This way, signals, and noise are distinguished and pattern recognition can be performed.
Object DetectionObject detection is the first stage of intelligent image analysis. Each object consists of several distinguishable properties that the software can use for classification. This, combined with an existing library of images, allows the software to compare, learn and determine valuable techniques to locate similarities and differences and provide accurate detection results. Object detection facilitates processes like automated damage assessment for insurance claims, property maintenance, store inventory management and more.
Face Recognition:Once the software recognizes an object, in this case — a face, the image is further processed to identify the person by comparing the facial data with existing data. The applications of this technology range across industries like healthcare, traffic management, manufacturing, HR management, security and so on.