Machine Learning and Deep learning
Machine Learning and Deep learning

Machine Learning and
Deep learning

Application of Statistics and Machine learning in business helps in business scalability and improving business processes. Artificial intelligence tools, ML algorithms and Deep Learning deployments have gained tremendous popularity in the business analytics community. Factors such as growing volumes of easy available data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom. We help organisations to understand how AI or ML can benefit their businesses and create end to end continuous learning solutions for them

STAGES IN ML DEVELOPMENT

1
 
 
 
 
DATA PREPARATION

This stage involves gathering data in a scalable manner and in a usable format by the machine learning models. In the data preparation phase we need to have data quality control measures, visualisations, hypothesis testing and use unsupervised techniques for exploratory data analysis

MODEL ALGORITHM

This is sometimes the hardest part in the entire flow. At this stage based on our data and overall business objectives we need to understand the most appropriate algorithm from a multitude of algorithms found in literature, design networks, validate assumptions of the model as well as verify the conceptual validity of using a model

 
 
 
 
2
3
 
 
 
 
EVALUATION

Even if the training model is good we need to be certain as to whether the model will perform as expected in a data on which the model has not been trained. Hence we test the model on evaluation data and compare performance with the training model. A large deterioration of performance indicates overfitting or issue with sampling and we need to go back to stage 1.

PARAMETER TUNING

Once we complete the evaluation phase we now come to the parameter tuning stage which is a method to improve our model performance by modulating the parameters (statistical/mathematical) of the model for example epochs or learning rates. Initial conditions might also play a pivotal role here. A deep understanding of statistical concepts as well as a grasp on the data and business requirements is need in this stage to create significant improvements

 
 
 
 
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