Python Scikit learn Programming with Coding Exercises

Course Overview

• Course Title: Python Scikit learn Programming with Coding Exercises
• Instructor: Dr Python
• Target Audience:
  • Data enthusiasts
  • Python developers
  • Professionals looking to break into machine learning
• Prerequisites:
  • Basic knowledge of Python programming
  • Familiarity with basic statistical concepts and linear algebra

Curriculum Highlights

• Key Topics Covered:
  • Introduction to Scikit-learn and its ecosystem
  • Data preprocessing and feature engineering
  • Supervised learning algorithms (linear regression, decision trees, support vector machines)
  • Unsupervised learning algorithms (k-means clustering, principal component analysis)
  • Model evaluation and hyperparameter tuning
  • Implementing cross-validation techniques
  • Building and deploying machine learning pipelines
• Key Skills Learned:
  • Preprocess data and perform feature engineering for machine learning models
  • Implement supervised and unsupervised learning algorithms using Scikit-learn
  • Evaluate, fine-tune, and deploy machine learning models
  • Build machine learning pipelines and use cross-validation techniques

Course Format

• Duration:
  • 1.5 hours on-demand video
• Format:
  • Self-paced online course
• Resources:
  • 2 practice tests
  • Assignments
  • 10 articles
  • Access on mobile and TV
  • Certificate of completion