Welcome to the Practical Machine Learning course! This path focuses on applying machine learning techniques to real-world problems. Here's what you'll learn:

📚 Learning Objectives

✅ Understand the workflow of machine learning projects
✅ Master data preprocessing and feature engineering
✅ Implement popular algorithms like linear regression, decision trees, and neural networks
✅ Learn model evaluation and hyperparameter tuning

🧱 Course Structure

  1. Data Exploration

    data_exploration
    - Use Python libraries (e.g., Pandas, Matplotlib) for data analysis - Identify patterns and outliers in datasets

  2. Model Training

    model_training
    - Split data into training and testing sets - Train models using scikit-learn or TensorFlow

  3. Evaluation & Optimization

    model_evaluation
    - Calculate metrics like accuracy, precision, and recall - Tune hyperparameters for better performance

🧠 Tips for Success

💡 Start with small datasets to practice concepts
💡 Use Jupyter Notebooks for interactive coding
💡 Collaborate on projects via GitHub

📚 Related Resources

Check out our theoretical course here to deepen your understanding of ML fundamentals.

For hands-on practice, try the Python for Data Science course next!

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