AI and Machine Learning Algorithms Using Python

How can you take your knowledge of machine learning (ML) concepts and how Python works within them to the next level?

This data science course will give you a strong grounding in the theories of machine learning, along with practical scenarios and experience of building, validating and deploying machine learning models.

You’ll learn basic machine learning and artificial intelligence (AI) concepts and get to understand relationships in complex data. Learn to use the Python programming language and examine how state-of-the-art machine learning algorithms are created and used in the products and services of tomorrow.

Learn the fundamentals of artificial intelligence and AI theory

What are the common concepts and theories driving AI technology today? The course will teach you core principles such as ML categories (such as supervised and unsupervised learning), the most common regression techniques, and how algorithms behave and learn in machines.

Gain hands-on experience in how to deploy machine learning models

Effectively deploying machine learning models is more of an art than science. On this course you’ll find out how to bridge the gap between IT and data science in putting a good model into production.

Discover how to use Python and Azure Notebooks to derive insights from models

Python and Azure Notebooks can be used to help you gather insights from ML models once they have been deployed. This course will show you how to collect output data, responses, request rates, failure rates and more with Python and Notebooks.

This course is ideal for anyone looking to use the principles of machine learning to lay the groundwork for artificial intelligence projects.

• 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