Aim

This course introduces participants to Scikit-learn, one of the most popular libraries for machine learning and data science in Python. The course covers essential concepts in data preprocessing, model building, and model evaluation. Participants will learn how to use Scikit-learn for supervised and unsupervised learning tasks, along with implementing algorithms such as regression, classification, clustering, and dimensionality reduction in real-world applications.

Program Objectives

• Learn the core principles of machine learning using Scikit-learn.
• Understand the process of data preprocessing and feature engineering in Scikit-learn.
• Implement supervised learning algorithms such as linear regression, decision trees, and support vector machines.
• Apply unsupervised learning algorithms like k-means clustering and PCA for dimensionality reduction.
• Gain hands-on experience using Scikit-learn for building, training, and evaluating machine learning models.

Participant Eligibility

• Students and professionals with basic knowledge of Python programming and machine learning concepts.
• Anyone interested in learning Scikit-learn for AI, data analysis, and machine learning applications.
• Developers, data scientists, and AI enthusiasts looking to enhance their skills in machine learning using Scikit-learn.

Program Outcomes

• Proficiency in using Scikit-learn for machine learning tasks, from data preprocessing to model evaluation.
• Hands-on experience implementing supervised and unsupervised learning models in Scikit-learn.
• Ability to evaluate model performance and optimize machine learning algorithms.
• Deep understanding of the Scikit-learn library, its modules, and advanced functionality.

Program Deliverables

• Access to e-LMS: Full access to course materials, datasets, and resources.
• Hands-on Project Work: Build machine learning models and evaluate them using Scikit-learn.
• Final Project: Apply Scikit-learn to solve a real-world problem with machine learning algorithms.
• Certification: Certification awarded after successful completion of the course and final project.
• e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

• Machine Learning Engineer
• Data Scientist
• AI Researcher
• Business Intelligence Analyst
• Data Analyst

Job Opportunities

• AI and Data Science Firms: Developing machine learning models for various industries using Scikit-learn.
• Tech Startups: Implementing machine learning algorithms for customer data analysis, fraud detection, and predictive analytics.
• Research Institutions: Using Scikit-learn for academic and industrial research in AI and machine learning.
• Financial Institutions: Applying machine learning techniques for financial modeling, risk analysis, and fraud detection.