Share Description
In this course, you will learn:
- The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem-specific.
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
