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Scikit-learn – Use in AI
Scikit-learn, machine learning, AI, data preprocessing, model validation, supervised learning, unsupervised learning, model optimization, real-world applications
Enroll now for early access of e-LMS
Online/ e-LMS
Mentor Based
Advanced
8 weeks
About
Scikit-learn – Use in AI is an extensive 8-week course designed for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals interested in mastering this essential machine learning library. The course provides a deep dive into data handling, supervised and unsupervised learning, model selection, and the integration of Scikit-learn with other AI technologies like TensorFlow and PyTorch.
Aim
The course aims to equip participants with a comprehensive understanding of Scikit-learn for implementing various machine learning models, from basic preprocessing to advanced algorithms in real-world applications.
Program Objectives
- Comprehensive Skill Development: Mastery of Scikit-learn for machine learning tasks.
- Practical Application: Ability to implement and evaluate machine learning models effectively.
- Innovative Problem Solving: Enhanced skills in using Scikit-learn for innovative solutions in AI.
Program Structure
- Introduction to Scikit-learn:
- Understanding Scikit-learn’s framework and its capabilities in AI.
- Setup and basic machine learning concepts relevant to Scikit-learn.
- Data Handling:
- Techniques for preprocessing data and managing model validation effectively.
- Supervised Learning:
- In-depth coverage of regression and classification models, including model tuning and evaluation.
- Unsupervised Learning:
- Exploration of clustering algorithms and dimensionality reduction techniques.
- Model Selection and Boosting:
- Advanced techniques for enhancing model performance using ensemble methods and feature selection.
- Advanced Applications:
- Application of Scikit-learn in text mining, natural language processing, and neural network integrations.
- Real-world Projects and Case Studies:
- Industry-specific applications and a capstone project involving real-world data and scenarios.
Participant’s Eligibility
- Advanced students and professionals in computer science, data science, and related fields looking to enhance their machine learning skills.
Program Outcomes
- Proficiency in Scikit-learn: Deep understanding and practical ability to use Scikit-learn in professional settings.
- Advanced Machine Learning Techniques: Skills in sophisticated machine learning techniques and their applications.
- Industry Readiness: Preparedness to apply AI skills in real-world industry scenarios.
Fee Structure
Standard Fee: INR 31,998 USD 858
Discounted Fee: INR 15999 USD 429
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesBatches
Live
Certificate
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
- Career Development: Guidance and support for careers in machine learning and data science.
- Industry Engagement: Insights from industry leaders using Scikit-learn in various sectors.
- Networking Opportunities: Connections with a network of professionals and potential employers in AI and data science.
Future Career Prospects
- Data Analyst: Utilizing Scikit-learn for comprehensive data analysis and insights.
- Machine Learning Engineer: Developing robust AI models using Scikit-learn.
- Research Scientist: Advancing the field of machine learning with innovative research.
Enter the Hall of Fame!
Take your research to the next level!
Achieve excellence and solidify your reputation among the elite!
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Recent Feedbacks In Other Workshops
The course was more theoretical than practical.
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