├── images ├── 30 12.png ├── 30 (4).png ├── 30 (5).png ├── 30 (7).png ├── IMG_20230323.png ├── Screenshot (313)3.png ├── IMG_20230323_134059_927.png ├── MicrosoftTeams-image (15).png └── 185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png ├── LICENSE ├── README.md ├── Microsoft Power BI └── README.md └── Machine Learning with Python and Azure └── README.md /images/30 12.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/30 12.png -------------------------------------------------------------------------------- /images/30 (4).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/30 (4).png -------------------------------------------------------------------------------- /images/30 (5).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/30 (5).png -------------------------------------------------------------------------------- /images/30 (7).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/30 (7).png -------------------------------------------------------------------------------- /images/IMG_20230323.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/IMG_20230323.png -------------------------------------------------------------------------------- /images/Screenshot (313)3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/Screenshot (313)3.png -------------------------------------------------------------------------------- /images/IMG_20230323_134059_927.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/IMG_20230323_134059_927.png -------------------------------------------------------------------------------- /images/MicrosoftTeams-image (15).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/MicrosoftTeams-image (15).png -------------------------------------------------------------------------------- /images/185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/HEAD/images/185755203-17945fd1-6b64-46f2-8377-1011dcb1a444.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 DataScience Nigeria 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DSN X Microsoft-30-days-of-Online-Study-2024 2 | 3 | [![Image](https://github.com/Data-Science-Nigeria/DSN-30-days-of-learning-2024/blob/main/images/30%20(7).png)]() 4 | 5 | 6 |

7 | 8 | 9 | 10 |

11 | 12 | Our 30 days courses 13 | - Sign up here:[click to register](https://t.co/5ayemRWgmE) 14 | - [Tweet about the courses!](https://twitter.com/intent/tweet?text=Join%20the%2030%20Days%20of%20Learning%20organized%20by%20%40dsn_ai_network%20and%20level%20up%20your%20Skills%0A%0ACourses%3A%0AMicrosoft%20Power%20BI%0AMachine%20Learning%20with%20Python%20and%20Azure%0A%0ADon%27t%20miss%20out%20on%20this%20amazing%20opportunity%21%20Register%20here%3A%20bit.ly%2FDSN24Prebootcamplearning%20%2330daysLearning) 15 | - For announcements, join our [Telegram channel](https://t.me/+YvF8TQvmYmRhNjdk) 16 | 17 | ## Taking the courses 18 | 19 | ### 2024 Cohort 20 | - Start: 12th August 2024 (Monday) 21 | - Registration link: https://bit.ly/DSN24Prebootcamplearning 22 | 23 | ### Self-paced Mode 24 | 25 | - All the materials of the course are freely available, allowing you to take the courses at your own pace. 26 | - Follow the suggested syllabus day by day. 27 | 28 | 29 | # Overview 30 | 31 | This 30-day learning program consists of four different courses: 32 | 33 | 1. Machine Learning with Python and Microsoft Azure 34 | 2. Microsoft Power BI 35 | 36 | Each course focuses on specific technologies and provides comprehensive knowledge and practical skills in their respective domains. 37 | 38 | ### Objective 39 | 40 | The aim of this learning program is to equip participants with practical knowledge and skills in the areas of Machine Learning with Microsoft Azure, Microsoft Power BI, and Machine Learning with Python. By the end of the program, participants will have a deep understanding of the core concepts, tools, and techniques related to each course. They will be able to utilize these technologies to solve complex problems, build applications, create visualizations, and deploy machine learning models in a production environment. 41 | 42 | ### Target Audience 43 | 44 | The program is designed for: 45 | - Tech enthusiasts who have a keen interest in acquiring skills related to machine learning with Microsoft Azure, Microsoft Power BI, and machine learning with Python. 46 | - Professionals seeking to enrich their knowledge in machine learning, app development, and data visualization. 47 | - Individuals looking to transition their careers into these technology domains. 48 | 49 | ### Pre-requisites 50 | 51 | To make the most out of this learning program, participants should have the following pre-requisites: 52 | 53 | **Microsoft Power BI:** 54 | - Basic understanding of data analysis concepts and techniques. 55 | 56 | **Machine Learning with Microsoft Azure:** 57 | - Prior exposure to machine learning concepts and techniques. 58 | - Familiarity with Microsoft Azure cloud platform. 59 | 60 | **Machine Learning with Python:** 61 | - Basic understanding of Python programming language. You can click on the [Python for beginners](https://learn.microsoft.com/en-us/training/paths/beginner-python/) module to learn Python. 62 | 63 | Please note that these prerequisites are specific to each course and are intended to ensure a foundational understanding of the topics covered. It is recommended to have the mentioned prerequisites in order to fully comprehend and engage with the course materials effectively. 64 | 65 | 66 | 67 | 68 | -------------------------------------------------------------------------------- /Microsoft Power BI/README.md: -------------------------------------------------------------------------------- 1 | # Microsoft Power BI 2 | 3 | Welcome to the Microsoft Power BI learning repository! This repository contains a series of modules that will help you get started with Power BI and explore its various capabilities. Whether you're new to Power BI or looking to enhance your skills, this learning path will guide you through the fundamentals and advanced topics. 4 | 5 | ## Description 6 | 7 | In this learning path, you will: 8 | 9 | - Learn about the capabilities of Microsoft Power BI 10 | - Explore what Power BI can do for you 11 | - Get started building with Power BI 12 | - Learn how to get and transform data in Power BI 13 | - Model and design data in Power BI 14 | - Build visuals and reports in Power BI 15 | - Distribute Power BI insights 16 | - Gain an understanding of Power BI administration 17 | 18 | This repository is structured into daily modules, providing you with a step-by-step learning experience. Each module focuses on a specific topic and includes links to relevant resources, tutorials, and examples to help you dive deeper into Power BI. 19 | 20 | ## Prerequisites 21 | 22 | Before starting this learning path, it is recommended to have a basic understanding of data analysis concepts and familiarity with Microsoft Excel. 23 | 24 | To complete the exercises and follow along with the tutorials, you will need: 25 | 26 | - Microsoft Power BI Desktop (latest version) 27 | - A Power BI account (you can sign up for a free account at [Power BI website](https://powerbi.microsoft.com/)) 28 | 29 | Please ensure that you have the necessary software and accounts set up before proceeding with the modules. 30 | 31 | ## Modules 32 | 33 | | Day | Module | 34 | |-----|-------------------------------------------------| 35 | | 1 | [Describe the capabilities of Microsoft Power BI](https://learn.microsoft.com/en-us/training/modules/introduction-power-bi/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-started-power-bi) | 36 | | 2 | [Explore what Power BI can do for you](https://learn.microsoft.com/en-us/training/modules/explore-power-bi-service/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-started-power-bi) | 37 | | 3 | [Discover Data Analysis](https://learn.microsoft.com/en-us/training/modules/data-analytics-microsoft/) | 38 | | 4 | [Get started building with Power BI](https://learn.microsoft.com/en-us/training/modules/get-started-with-power-bi/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-started-power-bi) | 39 | | 5 | [Get data in Power BI](https://learn.microsoft.com/en-us/training/modules/get-data/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-transform-data-power-bi) | 40 | | 6 | [Get data in Power BI](https://learn.microsoft.com/en-us/training/modules/get-data/) 41 | | 7 | [Clean, transform, and load data in Power BI](https://learn.microsoft.com/en-us/training/modules/clean-data-power-bi/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-transform-data-power-bi) | 42 | | 8 | [Clean, transform, and load data in Power BI](https://learn.microsoft.com/en-us/training/modules/clean-data-power-bi/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-transform-data-power-bi) | 43 | | 9 | [Clean, transform, and load data in Power BI](https://learn.microsoft.com/en-us/training/modules/clean-data-power-bi/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.get-transform-data-power-bi) | 44 | | 10 | [Design a semantic model in Power BI](https://learn.microsoft.com/en-us/training/modules/design-model-power-bi/) | 45 | | 11 | [Design a semantic model in Power BI](https://learn.microsoft.com/en-us/training/modules/design-model-power-bi/) | 46 | | 12 | [Design a semantic model in Power BI](https://learn.microsoft.com/en-us/training/modules/design-model-power-bi/) | 47 | | 13 | [Add measures to Power BI Desktop models](https://learn.microsoft.com/en-us/training/modules/dax-power-bi-add-measures/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.model-data-power-bi) | 48 | | 14 | [Add calculated tables and columns to Power BI Desktop models](https://learn.microsoft.com/en-us/training/modules/dax-power-bi-add-calculated-tables/?ns-enrollment-type=learningpath&ns-enrollment-id=learn.wwl.model-data-power-bi) | 49 | | 15 | [Use DAX time intelligence functions in Power BI Desktop models](https://learn.microsoft.com/en-us/training/modules/dax-power-bi-time-intelligence/) | 50 | | 16 | [Use DAX time intelligence functions in Power BI Desktop models](https://learn.microsoft.com/en-us/training/modules/dax-power-bi-time-intelligence/) | 51 | | 17 | [Optimize a model for performance in Power BI](https://learn.microsoft.com/en-us/training/modules/optimize-model-power-bi/) | 52 | | 18 | [Optimize a model for performance in Power BI](https://learn.microsoft.com/en-us/training/modules/optimize-model-power-bi/) | 53 | | 19 | [Design Power BI reports](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-reports/) | 54 | | 20 | [Design Power BI reports](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-reports/) | 55 | | 21 | [Design Power BI reports](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-reports/) | 56 | | 22 | [Configure Power BI report filters](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-filters/) | 57 | | 23 | [Enhance Power BI report designs for the user experience](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-user-experience/) | 58 | | 24 | [Enhance Power BI report designs for the user experience](https://learn.microsoft.com/en-us/training/modules/power-bi-effective-user-experience/) | 59 | | 25 | [Perform analytics in Power BI](https://learn.microsoft.com/en-us/training/modules/perform-analytics-power-bi/) | 60 | | 26 | [Perform analytics in Power BI](https://learn.microsoft.com/en-us/training/modules/perform-analytics-power-bi/) | 61 | | 27 | [Create and manage workspaces in Power BI](https://learn.microsoft.com/en-us/training/modules/create-manage-workspaces-power-bi/) | 62 | | 28 | [Create dashboards in Power BI](https://learn.microsoft.com/en-us/training/modules/create-dashboards-power-bi/) | 63 | | 29 | [Implement row-level security](https://learn.microsoft.com/en-us/training/modules/row-level-security-power-bi/) | 64 | | 30 | [Practice Exam](https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/practice/assessment?assessment-type=practice&assessmentId=48&practice-assessment-type=certification) | | 65 | 66 | ## Additional Resources 67 | 68 | Here are some additional resources related to preparing for PL-300 exam: 69 | 70 | - [Preparing for PL-300 - Prepare the Data (1 of 4)](https://learn.microsoft.com/en-us/shows/exam-readiness-zone/preparing-for-pl-300-prepare-the-data-1-of-4?source=recommendations) 71 | 72 | - [Preparing for PL-300 - Model the Data (2 of 4)](https://learn.microsoft.com/en-us/shows/exam-readiness-zone/preparing-for-pl-300-model-the-data-2-of-4) 73 | 74 | - [Preparing for PL-300 - Visualize and Analyze the Data (3 of 4)](https://learn.microsoft.com/en-us/shows/exam-readiness-zone/preparing-for-pl-300-visualize-and-analyze-the-data-3-of-4) 75 | 76 | - [Preparing for PL-300 - Deploy and Maintain Assets (4 of 4)](https://learn.microsoft.com/en-us/shows/exam-readiness-zone/preparing-for-pl-300-deploy-and-maintain-assets-4-of-4) 77 | 78 | -------------------------------------------------------------------------------- /Machine Learning with Python and Azure/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning with Python and Azure 2 | 3 | Welcome to the Machine Learning with Python and Azure guide! This section covers various topics related to machine learning using Python and Azure. It provides an overview of essential concepts and techniques, including data preprocessing, model training, evaluation, visualization, exploring Azure Machine Learning workspace resources, deploying models and performing hyperparameter tuning, you will gain a comprehensive understanding of machine learning in the Azure environment. 4 | 5 | ## Description 6 | 7 | ### Machine Learning with Python 8 | 9 | In this learning path, you will: 10 | 11 | - Explore Python notebooks for machine learning 12 | - Familiarize yourself with Python libraries such as NumPy, Pandas, and Matplotlib 13 | - Learn about time series analysis and visualization 14 | - Gain hands-on experience with popular machine learning libraries like Scikit-learn 15 | - Implement various algorithms such as linear regression, support vector machines, and decision trees 16 | - Perform data clustering and use K-nearest neighbors algorithm 17 | - Work with real-world datasets and perform data preprocessing 18 | - Utilize Python libraries for data visualization, including Seaborn 19 | 20 | ## Prerequisites 21 | 22 | Before starting this learning path, it is recommended to have a basic understanding of Python programming and some familiarity with data analysis concepts. You will need: 23 | 24 | - Python installed on your machine 25 | - Jupyter Notebook for running Python code 26 | - Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn installed 27 | 28 | Please ensure that you have the necessary prerequisites set up before proceeding with the modules. 29 | 30 | ### Additional Resources on Python: 31 | [Python for beginners](https://learn.microsoft.com/en-us/training/paths/beginner-python/) 32 | 33 | ### Machine Learning with Azure 34 | In this learning path, you will: 35 | 36 | - Explore Azure Machine Learning workspace resources and assets 37 | - Learn about developer tools for workspace interaction 38 | - Make data available in Azure Machine Learning 39 | - Discover the best classification models with Automated Machine Learning 40 | - Run training scripts and track model training with MLflow 41 | - Run pipelines in Azure Machine Learning 42 | - Perform hyperparameter tuning 43 | - Deploy models to manage online and batch endpoints 44 | - Get hands-on experience with the Azure Machine Learning SDK 45 | - Train machine learning models with Azure Machine Learning 46 | - Work with data and compute resources in Azure Machine Learning 47 | - Orchestrate machine learning workflows with pipelines 48 | - Deploy real-time machine learning services 49 | - Deploy batch inference pipelines 50 | - Explore differential privacy in machine learning 51 | - Explain machine learning models and detect/mitigate unfairness 52 | - Monitor models and detect data drift 53 | - Explore security concepts in Azure Machine Learning 54 | 55 | ## Prerequisites 56 | 57 | Before starting this learning path, it is recommended to have a basic understanding of machine learning concepts and some familiarity with Microsoft Azure. You will need: 58 | 59 | - An Azure subscription (you can sign up for a free trial at [Azure website](https://azure.microsoft.com/)) 60 | - Azure Machine Learning workspace (set up the workspace in Azure portal) 61 | 62 | Please ensure that you have the necessary prerequisites set up before proceeding with the modules. 63 | 64 | ## Modules 65 | 66 | | Day | Topic/Subject Matter | 67 | | ------| ----------------------------------------------------------------------------------- | 68 | | 1 | [ Introduction to Python](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/intro-to-python/&sa=D&source=editors&ust=1723135780850795&usg=AOvVaw1VI4WlSYOD8qhHnEEnCbeD) | 69 | | 2 | [Introduction to Jupyter](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/python-create-run-jupyter-notebook/&sa=D&source=editors&ust=1723135780851166&usg=AOvVaw2GwEn97pJMEAMUW6zAVa04) | 70 | | 3 | [Introduction to object-oriented programming with Python](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/python-object-oriented-programming/&sa=D&source=editors&ust=1723135780851288&usg=AOvVaw0MxSBMqpE2V29JnEMc66xL) | 71 | | 4 | [ Introduction to machine learning](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/introduction-to-machine-learning/&sa=D&source=editors&ust=1723135780851407&usg=AOvVaw1iCFfnznjq7YCwcdrXrL14) | 72 | | 5 | [Build classical machine learning models with supervised learning](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/introduction-to-classical-machine-learning/&sa=D&source=editors&ust=1723135780851496&usg=AOvVaw0hfv5BZ9e3p686HJveUbWO) | 73 | | 6 | [Introduction to data for machine learning](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/introduction-to-data-for-machine-learning/&sa=D&source=editors&ust=1723135780851591&usg=AOvVaw2242SDuwmUGS3dzcdFKPsz) | 74 | | 7 | [Explore and analyze data with Python](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/explore-analyze-data-with-python/&sa=D&source=editors&ust=1723135780851717&usg=AOvVaw0lZ9UVShihP6RPWVFul2CY) | 75 | | 8 | [Train and understand regression models in machine learning](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/understand-regression-machine-learning/&sa=D&source=editors&ust=1723135780851841&usg=AOvVaw2-MCSOdyQ8BEpjE32hDw4P) | 76 | | 9 | [Refine and test machine learning models](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/test-machine-learning-models/&sa=D&source=editors&ust=1723135780852000&usg=AOvVaw0EOK7fF63aB-IJ9JYTIun9) | 77 | | 10 | [Train and evaluate regression models](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/train-evaluate-regression-models/&sa=D&source=editors&ust=1723135780852085&usg=AOvVaw2HFqFBUF507wDc3xxFT_Ee) | 78 | | 11 | [ Create and understand classification models in machine learning](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/understand-classification-machine-learning/&sa=D&source=editors&ust=1723135780852197&usg=AOvVaw3bOQCRkyAL8irQD-OreFRa) | 79 | | 12 | [Select and customize architectures and hyperparameters using random forest](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/machine-learning-architectures-and-hyperparameters/&sa=D&source=editors&ust=1723135780852289&usg=AOvVaw3F8TY2eyr4yku-I24Q9-rD) | 80 | | 13 | [Confusion matrix and data imbalances](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/machine-learning-confusion-matrix/&sa=D&source=editors&ust=1723135780852377&usg=AOvVaw0ETMgYvCduAK8tttzhzHZ_) | 81 | | 14 | [Measure and optimize model performance with ROC and AUC](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/optimize-model-performance-roc-auc/&sa=D&source=editors&ust=1723135780852456&usg=AOvVaw3RgrWLTxteBv42krvBy0ck) | 82 | | 15 | [Train and evaluate clustering models](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/train-evaluate-cluster-models/&sa=D&source=editors&ust=1723135780852563&usg=AOvVaw1yh-v17ku2pWk8iSBBku5U) | 83 | | 16 | [Train and evaluate deep learning models](https://www.google.com/url?q=https://learn.microsoft.com/en-us/training/modules/train-evaluate-deep-learn-models/&sa=D&source=editors&ust=1723135780852677&usg=AOvVaw2ay17GStKgt1n11lV7gngw) | 84 | ## Machine Learning with Microsoft Azure 85 | | Day | Topic/Subject Matter | 86 | | ------| ----------------------------------------------------------------------------------- | 87 | | 17 | [Design a machine learning solution](https://learn.microsoft.com/en-us/training/paths/design-machine-learning-solution/) | 88 | | 18 | [Design a machine learning solution](https://learn.microsoft.com/en-us/training/paths/design-machine-learning-solution/) | 89 | | 19 | [Explore and configure the Azure Machine Learning workspace](https://learn.microsoft.com/en-us/training/paths/explore-azure-machine-learning-workspace/) | 90 | | 20 | [Explore and configure the Azure Machine Learning workspace](https://learn.microsoft.com/en-us/training/paths/explore-azure-machine-learning-workspace/) | 91 | | 21 | [Explore and configure the Azure Machine Learning workspace](https://learn.microsoft.com/en-us/training/paths/explore-azure-machine-learning-workspace/) | 92 | | 22 | [Explore and configure the Azure Machine Learning workspace](https://learn.microsoft.com/en-us/training/paths/explore-azure-machine-learning-workspace/) | 93 | | 23 | [Experiment with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/automate-machine-learning-model-selection-azure-machine-learning/) | 94 | | 24 | [Experiment with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/automate-machine-learning-model-selection-azure-machine-learning/) | 95 | | 25 | [Optimize model training with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/use-azure-machine-learning-pipelines-for-automation/) | 96 | | 26 | [Optimize model training with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/use-azure-machine-learning-pipelines-for-automation/) | 97 | | 27 | [Manage and review models in Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/manage-review-models-azure-machine-learning/) | 98 | | 28 | [Manage and review models in Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/manage-review-models-azure-machine-learning/) | 99 | | 29 | [Deploy and consume models with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/deploy-consume-models-azure-machine-learning/) | 100 | | 30 | [Experiment with Azure Machine Learning](https://learn.microsoft.com/en-us/training/paths/automate-machine-learning-model-selection-azure-machine-learning/) | 101 | 102 | ## Practice 103 | [Practice Assessment for Exam DP-100: Designing and Implementing a Data Science Solution on Azure](https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/practice/assessment?assessment-type=practice&assessmentId=62&practice-assessment-type=certification) 104 | --------------------------------------------------------------------------------