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.

Program Structure

Module 1: Introduction to Scikit-learn

Overview of Scikit-learn: Key features, modules, and use cases.
Installation and environment setup for Python and Scikit-learn.
Understanding Scikit-learn objects: datasets, estimators, transformers, and predictors.

Module 2: Data Preprocessing in Scikit-learn

Data cleaning: Handling missing values, outlier detection, and feature scaling.
Data transformation: Normalization, encoding categorical variables, and feature selection.
Splitting data into training and test sets using train_test_split.

Module 3: Supervised Learning with Scikit-learn

Introduction to supervised learning: Classification and regression tasks.
Building a linear regression model in Scikit-learn and evaluating performance.
Implementing decision trees and random forests for classification and regression.
Using support vector machines (SVM) and k-nearest neighbors (KNN) algorithms in Scikit-learn.

Module 4: Unsupervised Learning with Scikit-learn

Understanding clustering techniques: K-means, hierarchical clustering, and DBSCAN.
Implementing PCA (Principal Component Analysis) for dimensionality reduction.
Exploring DBSCAN and other density-based clustering methods.

Module 5: Model Evaluation and Tuning

Evaluating model performance: Cross-validation, accuracy, precision, recall, and F1 score.
Hyperparameter tuning with GridSearchCV and RandomizedSearchCV.
Improving model performance using ensemble methods such as AdaBoost and Gradient Boosting.

Module 6: Advanced Topics in Scikit-learn

Understanding model pipelines for streamlining data preprocessing and model training.
Implementing custom transformers in Scikit-learn.
Working with large datasets and parallel processing in Scikit-learn.

Final Project

Apply Scikit-learn to build and evaluate a machine learning model on a real-world dataset.
Example projects: Predictive modeling for sales forecasting, image classification, or customer segmentation.
Optimize and deploy the model for practical use cases.

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.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

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Artificial Intelligence(ai)

Scikit-learn – Use in AI Course

Course Overview

Aim

Program Objectives