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Participants will receive a certificate of completion at the end of the course on successfully clearing the assessment.
For more information contact:
Juhi Tuteja
E : Juhi.tuteja@in.ey.com
+91 8800158404
Avail two e-learning courses at INR 16,000 + GST
For group nominations kindly email at neha.tuteja@in.ey.com
Predictive analytics adopts a proactive approach to data. A general business intelligence tool uses data to learn about a customer or to identify trends in a business wherein, predictive analytics identifies how that customer will behave in a future situation and how they may react to the various interaction a business has with them. Predictive analytics empowers organizations to plan, which can turn uncertainty into an actionable insight with high probability.
This online predictive analytics course teaches learners to build Python models that identify patterns, forecast outcomes and support data-driven decisions.
Predictive modelling is one of the most crucial and essential components of Data Science. It is the final stage in Data Science where one or more algorithms generate predictions from historical data. Predictive modelling provides deeper insight into data and supports decisions that drive business performance.
There are more than 8.2 million developers who use Python making it one of the most popular languages for Machine Learning and IoT (Internet of Things) Apps. Technology companies and organizations such as IBM, Netflix, Google, YouTube, NASA, Amazon, Instagram and Facebook use Python in their applications. More and more companies are adopting Python as their core functionality and development language. Python certifications are widely recognized programming credentials worldwide.
Learners develop practical understanding of predictive modelling techniques and how different algorithms can be selected for classification and forecasting problems.
The program is suitable for analytics and data science professionals, machine learning learners, quality teams, automation specialists, entrepreneurs and graduates.
The curriculum covers linear and logistic regression, gradient descent, KNN, support vector machines, decision trees, random forests and a practical case study.