Machine Learning

Machine Learning Models

Specialized expertise Data Science helps Machine Learning models build – with an ideal and feasible approach. A Machine Learning model usually undergoes five stages up to completion.

• 1. Data Preparation:

  A Data Scientist collects, collects, and organizes data needed to build a viable machine learning model in line with the pre-defined problem. It involves cleaning data and converting it into the ideal format to zero in on the closest dataset.

• 2. Data Exploration:

  Using partially automated processes, data scientists visualize available data and obtain an overview to proceed with segregated data. It also encourages the use of graphics-driven data tools for visualization.

• 3. Feature Engineering:

  Feature engineering with AutoML verifies if all relevant input variables are available for optimal performance of the machine learning model. AutoML brings in basic-level automation.

• 4. Advanced Configuration:

  With innovative techniques and cutting-edge methods such as hyper-parameter optimization, Data Scientists ensure the model is the closest to accurate and of maximum possible confidence quality-levels.

• 5.Continuous Deployment:

  As in the software development cycle, the Data Sciences life cycle helps with continuous deployment – along with scope for maintenance. It helps repurpose training data that make the model viable and re-trainable.

Machine Learning is classified into three types based on the training data.

• Supervised Learning:

  Data scientists build these prototypes based on scenarios that involve well--designed input and output of the algorithm. The machine uses labeled data for detecting correlations to draw patterns.

• Unsupervised Learning:

  Models that typically rely on algorithms that make predictions outside the scope of input data to establish viable datasets to complete meaningful action. It operates with training on unlabeled datasets.

• Semi-supervised Learning:

  This constitutes open-ended learning where the model is free to explore and predict on unlabeled data - outside of the scope of algorithms. Though it is essentially a mix of the first two types, data scientists emphasize labeled training data scenarios.

Machine Learning: Use-cases

Predicting on outcomes and forecasting with raw business data, Machine Learning enables and drives organizational and day-to-day tasks or functions in more than one way:

CRM: Sales team can effectively shortlist sales funnels to respond to priority messages from qualified leads 

Business Intelligence:Drive analytics on Machine Learning to collate info on vital enterprise data-points

HRIS:Zero in on ideal candidate for open positions with smart recruitment solutions driven by ML algorithms

Assistants:Digital Assistants powered with supervised/unsupervised learning that can answer customer queries

Smart Cars:Machine Learning will automate next-gen transport with algorithms for intuitive self-driving cars

Enterprise ML Capabilities

Organizations look to innovation with Machine Learning across several functional units or teams. Below are a few strategic examples:

• Customer Support

  With Machine Learning data, businesses can track search patterns, purchase history, engagement, and click-through rates for offering specific customer support

• Product Positioning:

  Complex ML datasets on individual user preferences and the need-based approach in personalized services and offerings.

• Inventory Management:

  Intuitive ML models deployed across the supply chain can manage automation of inventory and close gaps in the supply chain.