- Module 1: A high-level overview of machine learning for people with little or no knowledge of computer science and statistics. You’ll be introduced to some essential concepts, explore data, and interactively go through the machine learning life-cycle - using Python to train, save, and use a machine learning model like we would in the real world.
- Explore how machine learning differs from traditional software
- Create and test a machine learning model
- Load a model and use it with new data
- Module 2: Supervised learning is a form of machine learning where an algorithm learns from examples of data. We progressively paint a picture of how supervised learning automatically generates a model that can make predictions about the real world. We also touch on how these models are tested, and difficulties that can arise in training them.
- Define supervised and unsupervised learning.
- Explore how cost functions affect the learning process.
- Discover how models are optimized by gradient descent.
- Experiment with learning rates, and see how they can affect training.
- Module 3: The power of machine learning models comes from the data that is used to train them. Through content and exercises, we explore how to understand your data, how to encode it so that the computer can interpret it properly, how to clean it of errors, and tips that will help you create models that perform well.
- Visualize large datasets with Exploratory Data Analysis (EDA)
- Clean a dataset of errors
- Predict unknown values using numeric and categorical data
- Module 4: Regression is arguably the most widely used machine learning technique, commonly underlying scientific discoveries, business planning, and stock market analytics. This learning material takes a dive into some common regression analyses, both simple and more complex, and provides some insight on how to assess model performance.
- Understand how regression works
- Work with new algorithms: Linear regression, multiple linear regression, and polynomial regression
- Understand the strengths and limitations of regression models
- Visualize error and cost functions in linear regression
- Understand basic evaluation metrics for regression
- Module 5: When we think of machine learning, we often focus on the training process. A small amount of preparation before this process can not only speed up and improve learning but also give us some confidence about how well our models will work when faced with data we have never seen before.
- Define feature scaling
- Create and work with test datasets
- Articulate how testing models can both improve and harm training
- Module 6: Classification means assigning items into categories, or can also be thought of automated decision making. Here we introduce classification models through logistic regression, providing you with a stepping-stone toward more complex and exciting classification methods.
- Discover how classification differs from classical regression
- Build models that can perform classification tasks
- Explore how to assess and improve classification models
- Module 7: Explore how altering the architecture of more complex models can bring about more effective results.
- Discover new model types– decision trees and random forests.
- Learn how model architecture can affect performance
- Practice working with hyperparameters to improve training effectiveness
- Module 8: How do we know if a model is good or bad at classifying our data? The way that computers assess model performance sometimes can be difficult for us to comprehend or can over-simplify how the model will behave in the real world. To build models that work in a satisfactory way, we need to find intuitive ways to assess them, and understand how these metrics can bias our view.
- Assess performance of classification models
- Review metrics to improve classification models
- Mitigate performance issues from data imbalances
- Module 9: Receiver operator characteristic curves are a powerful way to assess and fine-tune trained classification models. We introduce and explain the utility of these curves through learning content and practical exercises.
- Understand how to create ROC curves
- Explore how to assess and compare models using these curves
- Practice fine-tuning a model using characteristics plotted on ROC curves
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