Applied Machine Learning: Foundations

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Free Online Course: Applied Machine Learning: Foundations provided by LinkedIn Learning is a comprehensive online course, which lasts for 2-3 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from LinkedIn Learning. Applied Machine Learning: Foundations is taught by Derek Jedamski.

Overview
  • Generate impactful insights with the power of machine learning. Get the foundational skills needed to efficiently solve nearly any kind of machine learning problem.

Syllabus
  • Introduction

    • Leveraging machine learning
    • What you should know
    • What tools you need
    • Using the exercise files
    1. Machine Learning Basics
    • What is machine learning?
    • What kind of problems can this help you solve?
    • Why Python?
    • Machine learning vs. Deep learning vs. Artificial intelligence
    • Demos of machine learning in real life
    • Common challenges
    2. Exploratory Data Analysis and Data Cleaning
    • Why do we need to explore and clean our data?
    • Exploring continuous features
    • Plotting continuous features
    • Continuous data cleaning
    • Exploring categorical features
    • Plotting categorical features
    • Categorical data cleaning
    3. Measuring Success
    • Why do we split up our data?
    • Split data for train/validation/test set
    • What is cross-validation?
    • Establish an evaluation framework
    4. Optimizing a Model
    • Bias/Variance tradeoff
    • What is underfitting?
    • What is overfitting?
    • Finding the optimal tradeoff
    • Hyperparameter tuning
    • Regularization
    5. End-to-End Pipeline
    • Overview of the process
    • Clean continuous features
    • Clean categorical features
    • Split data into train/validation/test set
    • Fit a basic model using cross-validation
    • Tune hyperparameters
    • Evaluate results on validation set
    • Final model selection and evaluation on test set
    Conclusion
    • Next steps