Specialized Training


We have dedicated a good amount of time to design the advanced course in “Deep Learning” and “NLP” with extensive brainstorming to produce a great quality application-based curriculum that demonstrates real-time working models. Our focus on producing code examples such as ‘Object Detection’ (YOLO), Named Entity Recognition, Topic Modeling, RNN etc. which are meticulously worked upon by a team of experts to meet the complex and challenging industry requirements.

The below diagram depicts the Industry Standard “Data Science project work-flow”. All our courses meet the same standards.

Data Collection: Build automated Extraction pipelines to retrieve data from traditional Structured / Semi-structured sources (Oracle, IBM DB2, MySQL, JSON files etc) and Unstructured sources (several e-commerce platforms, social media, IOT devices etc). Applicable to OLTP/OLAP and modern BigData environments.

Train & Test Split: Avoid Data Snooping Bias by splitting the collected data to Train(70 to 80% of data) and Test (30 to 20 % of data). Treat “Train Set” only for model building activities where the machine does the pattern recognition. Once the model is finalized, evaluate it over Test Set.

Descriptive & Inferential Statistics: Analyse and Gain intuition of Data by applying Descriptive and Inferential Statistical techniques. Gain insights by Visualizing and understanding the relationship between Independent and Dependent variables for next steps.

EDA: This is one of the “Key Phase” to build a best performing model. Prepare the “Machine ready” data for feeding the Algorithms by performing Clean-up, feature selection, feature engineering and feature extraction.

Model building: Passing the Train data which is the outcome of EDA phase to the short-listed Machine Learning algorithms and build the respective models. Measure the accuracy of these models to meet the acceptance criteria.

Deployment: Once the accuracy measures reach the desired thresholds, the model can be deployed into production with a business sign off. The model can be used as a batch job or exposed as a web service to integrate with real time systems such as ERP’s, Enterprise Web and Mobile applications. The ML life cycle is an iterative process where the respective phases have to be revisited time and again until the business derived outcome is reached..

Data Collection: Build automated Extraction pipelines to retrieve data from traditional Structured / Semi-structured sources (Oracle, IBM DB2, MySQL, JSON files etc) and Unstructured sources (several e-commerce platforms, social media, IOT devices etc). Applicable to OLTP/OLAP and modern BigData environments.