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Microsoft

Create machine learning models

Microsoft via Microsoft Learn

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Overview

  • Module 1: Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.

    • In this module, you will learn:

    • Common data exploration and analysis tasks.
    • How to use Python packages like NumPy, Pandas, and Matplotlib to analyze data.
  • Module 2: Regression is a commonly used kind of machine learning for predicting numeric values.

    • In this module, you'll learn:

    • When to use regression models.
    • How to train and evaluate regression models using the Scikit-Learn framework.
  • Module 3: Train and evaluate classification models

    • In this module, you'll learn:

    • When to use classification
    • How to train and evaluate a classification model using the Scikit-Learn framework
  • Module 4: Clustering is a kind of machine learning that is used to group similar items into clusters.

    • In this module, you'll learn:

    • When to use clustering
    • How to train and evaluate a clustering model using the scikit-learn framework
  • Module 5: Train and evaluate deep learning models

    • In this module, you will learn:

    • Basic principles of deep learning
    • How to train a deep neural network (DNN) using PyTorch or Tensorflow
    • How to train a convolutional neural network (CNN) using PyTorch or Tensorflow
    • How to use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow

Syllabus

  • Module 1: Explore and analyze data with Python
    • Introduction
    • Explore data with NumPy and Pandas
    • Exercise - Explore data with NumPy and Pandas
    • Visualize data
    • Exercise - Visualize data with Matplotlib
    • Examine real world data
    • Exercise - Examine real world data
    • Knowledge check
    • Summary
  • Module 2: Train and evaluate regression models
    • Introduction
    • What is regression?
    • Exercise - Train and evaluate a regression model
    • Discover new regression models
    • Exercise - Experiment with more powerful regression models
    • Improve models with hyperparameters
    • Exercise - Optimize and save models
    • Knowledge check
    • Summary
  • Module 3: Train and evaluate classification models
    • Introduction
    • What is classification?
    • Exercise - Train and evaluate a classification model
    • Evaluate classification models
    • Exercise - Perform classification with alternative metrics
    • Create multiclass classification models
    • Exercise - Train and evaluate multiclass classification models
    • Knowledge check
    • Summary
  • Module 4: Train and evaluate clustering models
    • Introduction
    • What is clustering?
    • Exercise - Train and evaluate a clustering model
    • Evaluate different types of clustering
    • Exercise - Train and evaluate advanced clustering models
    • Knowledge check
    • Summary
  • Module 5: Train and evaluate deep learning models
    • Introduction
    • Deep neural network concepts
    • Exercise - Train a deep neural network
    • Convolutional neural networks
    • Exercise - Train a convolutional neural network
    • Transfer learning
    • Exercise - Use transfer learning
    • Knowledge check
    • Summary

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