Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.
제공자:
이 강좌에 대하여
Knowledge of basic mathematical concepts is important and some experience with Python is also beneficial.
배울 내용
How to plan and create a working environment for data science workloads on Azure and how to run data experiments and train predictive models.
귀하가 습득할 기술
- Manage Azure resources for machine learning
- Deploy and operationalize machine learning solutions
- Run experiments and train models
- Implement responsible machine learning
Knowledge of basic mathematical concepts is important and some experience with Python is also beneficial.
제공자:

Microsoft
Our goal at Microsoft is to empower every individual and organization on the planet to achieve more.
강의 계획표 - 이 강좌에서 배울 내용
Explore data and create models to predict numeric values
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. n this module, you will learn how to use Python to explore, visualize, and manipulate data.You will also learn how regression can be used to create a machine learning model that predicts numeric values. You will use the scikit-learn framework in Python to train and evaluate a regression model.
Train and evaluate classification and clustering models
Classification is a kind of machine learning used to categorize items into classes. In this module, you will learn how classification can be used to create a machine learning model that predicts categories, or classes. You will use the scikit-learn framework in Python to train and evaluate a classification model. You will also learn how clustering can be used to create unsupervised machine learning models that group data observations into clusters. You will use the scikit-learn framework in Python to train a clustering model.
Train and evaluate deep learning models
In this module, you will learn about the fundamental principles of deep learning, and how to create deep neural network models using PyTorch or Tensorflow. You will also explore the use of convolutional neural networks to create image classification models.
Microsoft Azure 데이터 과학 어소시에이트(DP-100) 전문 자격증 정보
This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. This Professional Certificate teaches learners how to create end-to-end solutions in Microsoft Azure. They will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This Professional Certificate teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. By the end of this program, you will be ready to take the DP-100: Designing and Implementing a Data Science Solution on Azure.

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