Supervised Learning Essential Training

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01 Introduction

001 Supervised machine learning and the technology boom.mp4

00:59

002 Using the exercise files.mp4

00:58

003 What you should know.mp4

00:34

02 Supervised Learning with Python

001 What is supervised learning .mp4

03:09

002 Python supervised learning packages.mp4

00:56

003 Predicting with supervised learning.mp4

02:05

03 Regression Modeling

001 Defining logistic and linear regression.mp4

02:51

002 Steps to prepare data for modeling.mp4

04:31

003 Checking your dataset for assumptions.mp4

07:18

004 Creating a linear regression model.mp4

02:51

005 Creating a logistic regression model.mp4

04:28

006 Evaluating regression model predictions.mp4

02:40

04 Decision Trees

001 Identify common decision trees.mp4

01:55

002 Splitting data and limiting decision tree depth.mp4

03:41

003 How to build a decision tree.mp4

02:03

004 Creating your first decision trees.mp4

02:49

005 Analyzing decision tree performance.mp4

05:01

006 Exploring how ensemble methods create strong learners.mp4

01:55

05 K-Nearest Neighbors

001 Discovering your k-nearest neighbors.mp4

03:07

002 Whats the big deal about k.mp4

02:07

003 How to assemble a KNN model.mp4

01:53

004 Building your own KNN.mp4

07:13

005 Deciphering KNN model metrics.mp4

04:12

006 Searching for the best model.mp4

03:13

06 Neural Networks

001 Biological vs. artificial neural networks.mp4

03:05

002 Preprocessing data for modeling.mp4

02:25

003 How neural networks find patterns in data.mp4

02:08

004 Assembling your neural networks.mp4

02:55

005 Comparing networks and selecting final models.mp4

01:56

07 Conclusion

001 Ethical overview.mp4

02:57

002 How can I keep developing my skills in supervised learning .mp4

00:33

Description

Data scientists and ML/AI students may need some practical experience with supervised learning algorithms. In this course, instructor Ayodele Odubela teaches you to apply models you’ve created to new data and to assess model performance. First, Ayodele outlines what supervised learning is and how to make predictions using labeled training data. She gives you an overview of the logistic regression algorithm, how to build a linear model in Python, and how to calculate model metrics. Next, Ayodele helps you create your first decision trees as well as k-nearest neighbors models using GridSearch. Ayodele covers how you can create artificial neural networks that are foundational for most deep learning work. She concludes with an ethical AI overview and asks you to consider the impact of your models.

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.