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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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