Getting Started with Machine Learning in Python [Video]
Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills.
In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.
You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.
If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you!
All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python-Style and Approach
This extensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you. A practical, hands-on course packed with step-by-step instructions, working examples, and helpful advice. You will learn how Machine Learning can be used to create artificial intelligence.
|Course Length||2 hours 53 minutes|
|Date Of Publication||28 Sep 2018|
|Dealing with Missing Values – An Example|
|Standardization and Normalization to Deal with Variables with Different Scales|
|Eliminating Duplicate Entries|
|Understanding Logistic Regression – Your First Classifier|
|Applying Logistic Regression to the Iris Classification Task|
|Closing Our First Machine Learning Pipeline with a Simple Model Evaluator|
|Understanding Linear Regression – Your First Regressor|
|Applying Linear Regression to the Boston House Price Task|
|Evaluating Numerical Predictions with Least Squares|