Development

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
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