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ServiceNow 10 - 12 Week Certification Course
Learn From Industry Experts
* Only Limited Slots Per Batch
* No Prior Coding Experience Required
Join Our Upcoming Masterclass, For Free !
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
- No prior experience with ServiceNow is required
- This course is suitable for individuals with a basic understanding of IT service management and workflow concepts.
- Familiarity with scripting or programming languages (JavaScript, for instance) can be beneficial for the development-focused modules.
Course objectives
- To provide a solid foundation in data science and machine learning concepts.
- To develop proficiency in data preprocessing, analysis, and visualization.
- To explore various machine learning algorithms and their practical implementations.
- To empower students to build predictive models and make data-driven decisions.
- To introduce advanced topics such as deep learning and natural language processing.
- To foster critical thinking and problem-solving skills through real-world projects.
Who can apply?
- Undergraduate or Graduate Students from various academic backgrounds who want to learn about data science and machine learning.
- Professionals from diverse fields such as IT, engineering, finance, healthcare, marketing, and more, who wish to transition into data science or machine learning roles or enhance their existing skills.
- Data Analysts who want to expand their skillset and move into more advanced data science and machine learning roles.
- Programmers with programming experience (preferably in Python) who want to specialize in data science and machine learning.
- Statisticians who want to apply their statistical knowledge to practical data science and machine learning applications.
- Researchers interested in utilizing data science and machine learning techniques to analyze research data or solve complex problems in their field.
- Career Changers who are looking to switch careers and enter the data science or machine learning industry, even if they have no prior experience in the field.
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
- Understanding the data science lifecycle.
- Data collection, cleaning, and exploration.
- Data visualization techniques.
Descriptive and inferential statistics.
Probability distributions.
Hypothesis testing and confidence intervals.
Data normalization and scaling.
Handling missing data and outliers.
Feature engineering.
Supervised, unsupervised, and reinforcement learning.
Model evaluation and validation.
Cross-validation techniques.
Linear and logistic regression.
Decision trees and random forests.
Support Vector Machines (SVM).
Clustering algorithms (K-means, DBSCAN).
Dimensionality reduction (PCA).
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.
Text preprocessing and tokenization.
Sentiment analysis.
Named Entity Recognition (NER).
Text classification with deep learning.
Students will work on hands-on projects throughout the course to apply their knowledge to real data science and machine learning problems.
Discussion on the ethical use of data and machine learning algorithms.
Bias and fairness in machine learning.
Exposure to industry-standard tools and workflows.
Guest lectures and case studies from industry experts.
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 through quizzes, assignments, and project submissions. Final examination.
- Evaluation of the capstone project.
FAQ
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what kind of classes do you offer?
*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews
I never boxed before can i do it?
*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews
what is your opening hours?
*Initial one-to-one consultation, Health & Fitness Assasments Bespoke training program planing, Custom Nutrition plan & recipes. Weekly Progress Reviews
