Introduction to machine learning: Python has become a dominant language for doing data analysis with machine learning. Learn how to leverage Python and associated libraries in Jupyter Notebooks run on Azure Notebooks to predict patterns and identify trends. This learning path can help you prepare for the Microsoft Certified: Azure Developer Associate certification.

Python for Machine Learning:

Python is a popular programming language for machine learning due to its simplicity and a vast ecosystem of libraries and tools. An introduction to machine learning with Python typically covers key libraries like NumPy, pandas, Matplotlib, and scikit-learn, which are essential for data manipulation, visualization, and building machine learning models.

Azure Notebooks:

Azure Notebooks is a cloud-based Jupyter Notebook service provided by Microsoft Azure. Jupyter Notebooks are interactive documents that combine code, text, and visualizations. Azure Notebooks allow you to run Python code and perform data analysis, including machine learning, in a collaborative and scalable environment without the need to install anything locally.

Here’s a basic outline of what an “Introduction to Machine Learning with Python and Azure Notebooks” course or tutorial might cover:

1. Setting Up Azure Notebooks: An introduction to creating an Azure Notebooks account and setting up your workspace.
2. Python Basics: An overview of Python programming basics, including variables, data types, and control structures.
3. Data Manipulation: Using libraries like NumPy and pandas to load, clean, and preprocess data for machine learning.
4. Data Visualization: Exploring and visualizing data using libraries like Matplotlib and Seaborn to gain insights.
5. Machine Learning Basics: An introduction to the fundamentals of machine learning, including supervised and unsupervised learning, and the concept of training and testing data.
6. Building a Machine Learning Model: Using scikit-learn, a popular machine learning library in Python, to create and train a basic machine learning model.
7. Evaluating Model Performance: Techniques for assessing the performance of a machine learning model, including metrics like accuracy, precision, recall, and F1-score.
8. Azure Notebooks Integration: Demonstrating how to leverage Azure Notebooks for running machine learning experiments, storing data, and collaborating with others.
9. Advanced Topics: Depending on the course’s depth, you may cover advanced topics such as feature engineering, hyperparameter tuning, and deploying models to Azure.
10. Practical Projects: Hands-on projects or exercises that allow learners to apply what they’ve learned in real-world scenarios.