Data-Centric Machine Learning with Python: Hands-On Guide

Course Overview

• Course Title: Data-Centric Machine Learning with Python: Hands-On Guide
• Instructor: Meta Brains
• Target Audience:
  • Aspiring data scientists
  • Analysts
  • Technology professionals
• Prerequisites:
  • Basic understanding of Python programming
  • Familiarity with high school-level math concepts
  • A computer with Python and necessary libraries installed

Curriculum Highlights

• Key Topics Covered:
  • Introduction to machine learning paradigms
  • Python's data science ecosystem
  • Data preparation stages
  • Exploratory data analysis
  • Handling missing values
  • Feature engineering
  • Preprocessing data
  • Supervised learning techniques
  • Regression and classification approaches
  • Evaluation metrics
  • Model selection and validation techniques
  • Cross-validation
  • Hyperparameter tuning
  • Unsupervised learning methods
  • Clustering
  • Anomaly detection
  • Text analysis
  • Image classification
  • Recommendation systems
  • Loan prediction project
• Key Skills Learned:
  • Preprocess data effectively for machine learning models
  • Perform exploratory data analysis using Python libraries
  • Differentiate between supervised and unsupervised learning
  • Build and optimize machine learning algorithms in Python
  • Create insightful data visualizations and plots
  • Apply feature engineering techniques to improve models
  • Evaluate model performance with appropriate metrics
  • Solve real-world problems using machine learning workflows

Course Format

• Duration: 3.5 hours on-demand video
• Format: Self-paced online course
• Resources:
  • Access on mobile and TV
  • Certificate of completion