Applied Machine Learning: Algorithms

Intermediate 0(0 Ratings) 0 Students enrolled
Created by Skill Central Last updated Sun, 28-Feb-2021 English
What will i learn?
  • Models vs. algorithms
  • Cleaning continuous and categorical variables
  • Tuning hyperparameters
  • Pros and cons of logistic regression
  • Fitting a support vector machines model
  • When to consider using a multilayer perceptron model
  • Using the random forest algorithm
  • Fitting a basic boosting model

Curriculum for this course
28 Lessons 02:01:38 Hours
Introduction
3 Lessons 00:05:08 Hours
  • The power of algorithms in machine learning 00:02:12
  • What you should know 00:01:23
  • What tools you need 00:01:33
Review of Foundations
5 Lessons 00:24:45 Hours
  • Defining model vs. algorithm 00:02:35
  • Process overview 00:03:32
  • Clean continuous variables 00:07:32
  • Clean categorical variables 00:06:48
  • Split into train, validation, and test set 00:04:18
Logistic Regression
4 Lessons 00:20:48 Hours
  • What is logistic regression? 00:03:11
  • When should you consider using logistic regression? 00:03:29
  • What are the key hyperparameters to consider? 00:04:48
  • Fit a basic logistic regression model 00:09:20
Multi-layer Perception
4 Lessons 00:20:17 Hours
  • What is a multi-layer perceptron? 00:03:33
  • When should you consider using a multi-layer perceptron? 00:03:08
  • What are the key hyperparameters to consider? 00:05:31
  • Fit a basic multi-layer perceptron model 00:08:05
Random forest
4 Lessons 00:13:26 Hours
  • What is Random Forest? 00:04:13
  • When should you consider using Random Forest? 00:02:07
  • What are the key hyperparameters to consider? 00:02:41
  • Fit a basic Random Forest model 00:04:25
Boosting
4 Lessons 00:17:25 Hours
  • What is boosting? 00:05:23
  • When should you consider using boosting? 00:02:46
  • What are the key hyperparameters to consider boosting? 00:03:53
  • Fit a basic boosting model 00:05:23
Summary
3 Lessons 00:19:49 Hours
  • Why do you need to consider so many different models? 00:04:09
  • Conceptual comparison of algorithms 00:04:06
  • Final model selection and evaluation 00:11:34
The Test Questions
1 Lessons 00:00:00 Hours
  • Questions: 00:00:00
Requirements
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Description

In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course—the second and final installment in the series—Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.

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Includes:
  • 02:01:38 Hours On demand videos
  • 28 Lessons
  • Access on mobile and tv
  • Full lifetime access
  • Certificate of completion
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