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ServiceNow 10 - 12 Week Certification Course

Learn From Industry Experts

* Only Limited Slots Per Batch

* No Prior Coding Experience Required

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Join Our Upcoming Masterclass, For Free !

KEY FEATURES

Live Classes

10 - 12 Weeks

Lab Support

For Practicals

Industry Expert

Learn From Top Industry Expert

Live Projects

Work On Capstone Projects

Overview of this Certification Course

This ServiceNow Administration and Development course provides a comprehensive exploration of the ServiceNow platform, a leading cloud-based IT Service Management (ITSM) and business workflow automation solution. ServiceNow is widely adopted by organizations to streamline their IT operations, enhance customer service, and drive digital transformation. This course covers both the administrative and development aspects of ServiceNow, enabling students to effectively configure, customize, and create applications on the platform.

Prerequisites of this Course

Course objectives

Who can apply?

Our Hiring Partners

Skills You Learn From this Course

Data Handling and Preprocessing

Statistical Analysis

Data Visualization

Machine Learning Fundamentals

Machine Learning Algorithms

Deep Learning and Neural Networks

Natural Language Processing (NLP)

Ethical Considerations

And Many More...

Course Curriculum

Chapter 1. Introduction to Data Science:

  • Understanding the data science lifecycle.
  • Data collection, cleaning, and exploration.
  • Data visualization techniques.

Chapter 2. Statistics for Data Science:

 

 

  • Descriptive and inferential statistics.

  • Probability distributions.

  • Hypothesis testing and confidence intervals.

Chapter 3 Data Preprocessing:

 

 

  • Data normalization and scaling.

  • Handling missing data and outliers.

  • Feature engineering.

Chapter 4 Machine Learning Fundamentals:

 

 

  • Supervised, unsupervised, and reinforcement learning.

  • Model evaluation and validation.

  • Cross-validation techniques.

Chapter 5 Machine Learning Algorithms:

 

 

  • Linear and logistic regression.

  • Decision trees and random forests.

  • Support Vector Machines (SVM).

  • Clustering algorithms (K-means, DBSCAN).

  • Dimensionality reduction (PCA).

Chapter 6 Deep Learning and Neural Networks:

 

 

  • Introduction to artificial neural networks.

  • Building and training deep neural networks.

  • Convolutional Neural Networks (CNN) for image data.

  • Recurrent Neural Networks (RNN) for sequential data.

Chapter 7 Natural Language Processing (NLP):

 

 

  • Text preprocessing and tokenization.

  • Sentiment analysis.

  • Named Entity Recognition (NER).

  • Text classification with deep learning.

Chapter 8 Real-World Projects:

 

 

  • Students will work on hands-on projects throughout the course to apply their knowledge to real data science and machine learning problems.

Chapter 9 Ethical Considerations:

 

 

  • Discussion on the ethical use of data and machine learning algorithms.

  • Bias and fairness in machine learning.

Chapter 10 Industry Best Practices:

 

 

  • Exposure to industry-standard tools and workflows.

  • Guest lectures and case studies from industry experts.

Chapter 11 . Capstone Project:

 

  • In the final part of the course, students will work on a capstone project where they will apply their acquired skills to solve a complex problem in data science or machine learning.

Continuous Assessment:

  • Continuous assessment through quizzes, assignments, and project submissions. Final examination.
  • Evaluation of the capstone project.

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*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews

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