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Scikit-learn – Use in AI Course

Original price was: INR ₹31,998.00.Current price is: INR ₹15,999.00.

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 interested in mastering Scikit-learn, one of the most popular machine learning libraries. This course covers everything from data preprocessing and model validation to supervised and unsupervised learning, model optimization, and real-world AI applications. Participants will also explore how Scikit-learn integrates with other AI technologies like TensorFlow and PyTorch.

Scikit-learn – Use in AI

Master Machine Learning with Scikit-learn for Real-World AI Solutions

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.

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Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

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