YoVDO

Understand data science for machine learning

Offered By: Microsoft via Microsoft Learn

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Machine Learning Courses Data Science Courses Supervised Learning Courses Regression Analysis Courses Random Forests Courses ROC Curve Courses Model Evaluation Courses Classification Models Courses

Course Description

Overview

  • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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.

    • In this module, you will:

    • 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

Syllabus

  • Module 1: Introduction to machine learning
    • Introduction
    • What are machine learning models?
    • Exercise - Create a machine learning model
    • What are inputs and outputs?
    • Exercise - Visualize inputs and outputs
    • How to use a model
    • Exercise - Use machine learning models
    • Knowledge check
    • Summary
  • Module 2: Build classical machine learning models with supervised learning
    • Introduction
    • Define supervised learning
    • Exercise - Implement supervised learning
    • Minimize model errors with cost functions
    • Exercise - Optimize a model by using cost functions
    • Optimize models by using gradient descent
    • Exercise - Implement gradient descent
    • Knowledge check
    • Summary
  • Module 3: Introduction to data for machine learning
    • Introduction
    • Good, bad, and missing data
    • Exercise - Visualize missing data
    • Examine different types of data
    • Exercise - Work with data to predict missing values
    • One-hot vectors
    • Exercise - Predict unknown values using one-hot vectors
    • Knowledge check
    • Summary
  • Module 4: Train and understand regression models in machine learning
    • Introduction
    • What is regression?
    • Exercise - Train a simple linear regression model
    • Multiple linear regression and R-squared
    • Exercise - Train a multiple linear regression model
    • Polynomial Regression
    • Exercise - Polynomial regression
    • Knowledge check
    • Summary
  • Module 5: Refine and test machine learning models
    • Introduction
    • Normalization and standardization
    • Exercise – Feature scaling
    • Test and training datasets
    • Exercise - Test and train datasets
    • Nuances of test sets
    • Exercise – Test set nuances
    • Knowledge check
    • Summary
  • Module 6: Create and understand classification models in machine learning
    • Introduction
    • What are classification models?
    • Exercise - Build a simple logistic regression model
    • Assessing a classification model
    • Exercise - Assessing a logistic regression model
    • Improving classification models
    • Exercise - Improving classification models
    • Knowledge check
    • Summary
  • Module 7: Select and customize architectures and hyperparameters using random forest
    • Introduction
    • Decision trees and model architecture
    • Exercise - Decision trees and model architecture
    • Random forests and selecting architectures
    • Exercise - Selecting random forest architectures
    • Hyperparameters in classification
    • Exercise - Hyperparameter tuning with random forests
    • Knowledge check
    • Summary
  • Module 8: Confusion matrix and data imbalances
    • Introduction
    • Confusion matrices
    • Exercise – Building a confusion matrix
    • Data imbalances
    • Exercise - Resolving biases in a classification model
    • Cost functions versus evaluation metrics
    • Exercise - Multiple metrics and ROC curves
    • Knowledge check
    • Summary
  • Module 9: Measure and optimize model performance with ROC and AUC
    • Introduction
    • Analyze classification with receiver operator characteristic curves
    • Exercise - Evaluate ROC curves
    • Compare and optimize ROC curves
    • Exercise - Tune the area under the curve
    • Knowledge check
    • Summary

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