Microsoft Future Ready: Principles of Machine Learning with Python Programming

This course is part of the Ethics Laws and Implementing an AI Solution on Microsoft Azure ExpertTrack, helping you understand AI ethics and laws while developing advanced AI and machine learning skills.

During this course, you’ll learn the theory of machine learning before gaining practical experience in building, validating and deploying machine learning models.

You’ll also delve into Python and Azure Notebooks, two powerful tools to help you learn how to build and derive insights.

Learn the fundamentals of machine learning and AI theory

This course will teach you the concepts and theories which drive AI technology today. You’ll learn the core principles of machine learning categories, including supervised and unsupervised learning, regression techniques and how algorithms behave and learn in machines.

Gain practical experience in how to deploy machine learning models

You’ll learn how to effectively deploy machine learning models, and understand the gap between IT and data science in putting a model into production. This will further your knowledge in the field and help you understand how this practice sits in the workplace.

Get hands-on experience using Python programming and Azure Notebooks

In this course you’ll learn the importance of evaluating your data before developing algorithms. You’ll explore how to prepare and clean data sets as well as feature engineering techniques.

Following the theory, you’ll get hands-on experience of the ways you can evaluate data using Python and Azure Notebooks.

By the end of the course, you’ll understand the basic machine learning concepts, and gain experience using powerful tools such as Python with state-of-the-art machine learning algorithms. This knowledge will further help you understand the relationship in complex data.

This course is part of the Microsoft Professional Program Certificate in Data Science and the Microsoft Professional Program in Artificial Intelligence. It provides a foundational understanding of machine learning using python, useful for anyone new to learning python, or wishing to use python to build machine learning solutions.

The practical elements of this course involve building end-to-end workflows by combining the concepts and algorithms that will be introduced throughout the course. For the most part, you’ll be given the code you need to complete the exercises, but a basic knowledge of Python syntax will improve your understanding of what’s going on in the labs and demonstrations.

• Introduction to Course and Machine Learning
• Course Introduction
• Introduction to Machine Learning
• Exploratory Data Analysis for Regression
• Visualisation for High Dimensions
• Wrapping Up the Week
• Data Exploration & Preparation
• Exploratory Data Analysis for Classification
• Data Cleaning
• Data Preparation
• Data Preparation and Cleaning using Python
• Feature Engineering
• Weekly Wrap-Up
• Regression & Classification
• Regression
• Putting Regression Concepts Into Practice
• Classification
• RoC Curves
• Putting Classification Concepts Into Practice
• Weekly Wrap-Up
• Principles & Techniques of Model Improvement
• Principles of Model Improvement
• Techniques for Improving Models
• Cross Validation
• Dimensionality Reduction
• Introduction to Decision Trees
• Ensemble Methods: Boosting
• Weekly Wrap-Up
• Machine Learning Algorithms & Unsupervised Learning
• Ensemble Methods: Descent & Decision Forests
• Advanced Machine Learning Algorithm: Neural Networks
• Advanced Machine Learning Algorithm: SVMs
• Advanced Machine Learning Algorithm: Naive Bayes Models
• Unsupervised Machine Learning
• Unsupervised Machine Learning Labs
• Wrapping up the Course