Course Overview

Scikit-learn – Use in AI is an 8-week comprehensive course designed for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals. The course offers in-depth coverage of Scikit-learn, one of the most popular machine learning libraries. Participants will learn everything from data preprocessing and model validation to supervised and unsupervised learning, model optimization, and real-world AI applications. The course also covers the integration of Scikit-learn with other AI technologies like TensorFlow and PyTorch.

Course Goals

The course aims to equip participants with the skills to effectively use Scikit-learn for implementing a wide range of machine learning models. The focus is on practical applications, from basic data handling to deploying advanced algorithms in real-world settings.

Program Objectives

• Comprehensive Skill Development: Master Scikit-learn for a variety of machine learning tasks, from data preprocessing to model selection.
• Practical Application: Develop the ability to implement, tune, and evaluate machine learning models for real-world problems.
• Innovative Problem Solving: Enhance problem-solving skills using Scikit-learn for innovative AI solutions.

Program Structure

• Module 1: Introduction to Scikit-learn
  • Understanding Scikit-learn’s framework and capabilities in machine learning
  • Basic machine learning concepts relevant to Scikit-learn
  • Setting up the Scikit-learn environment
• Module 2: Data Handling
  • Techniques for preprocessing data (handling missing data, scaling, encoding, etc.)
  • Managing model validation with cross-validation techniques
• Module 3: Supervised Learning
  • Exploration of regression and classification models (e.g., linear regression, decision trees, SVMs)
  • Tuning and evaluating models for optimal performance
• Module 4: Unsupervised Learning
  • Introduction to clustering algorithms (e.g., K-means, DBSCAN)
  • Dimensionality reduction techniques (e.g., PCA, t-SNE)
• Module 5: Model Selection and Boosting
  • Feature selection and engineering techniques
  • Boosting methods (e.g., AdaBoost, Gradient Boosting) to enhance model performance
• Module 6: Advanced Applications
  • Using Scikit-learn in text mining and natural language processing (NLP)
  • Integrating Scikit-learn with neural networks in TensorFlow and PyTorch
• Module 7: Real-World Projects and Case Studies
  • Industry-specific applications of Scikit-learn in healthcare, finance, and other sectors
  • Capstone project involving real-world data and machine learning model development

Eligibility

• Advanced Students: M.Tech, M.Sc, MCA students in computer science, AI, or data science.
• Professionals: IT and data science professionals looking to enhance their machine learning skills with Scikit-learn.

Learning Outcomes

• Proficiency in Scikit-learn: Gain a deep understanding and hands-on ability to use Scikit-learn in professional environments.
• Advanced Machine Learning Techniques: Develop skills in sophisticated machine learning techniques like model optimization, boosting, and feature selection.
• Industry Readiness: Be prepared to apply AI and machine learning skills in real-world industry scenarios.