Applied Machine Learning: Algorithms

Applied Machine Learning: Algorithms
Applied Machine Learning: Algorithms

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 24m | 348 MB
eLearning | Skill level: All Levels


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.

Topics include:

  • 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
+ Table of Contents

1 The power of algorithms in machine learning
2 What you should know
3 What tools you need
4 Using the exercise files
5 Defining model vs. algorithm
6 Process overview
7 Clean continuous variables
8 Clean categorical variables
9 Split into train, validation, and test set
10 What is logistic regression
11 When should you consider using logistic regression
12 What are the key hyperparameters to consider
13 Fit a basic logistic regression model
14 What is Support Vector Machine
15 When should you consider using SVM
16 What are the key hyperparameters to consider
17 Fit a basic SVM model
18 What is a multi-layer perceptron
19 When should you consider using a multi-layer perceptron
20 What are the key hyperparameters to consider
21 Fit a basic multi-layer perceptron model
22 What is Random Forest
23 When should you consider using Random Forest
24 What are the key hyperparameters to consider
25 Fit a basic Random Forest model
26 What is boosting
27 When should you consider using boosting
28 What are the key hyperparameters to consider boosting
29 Fit a basic boosting model
30 Why do you need to consider so many different models
31 Conceptual comparison of algorithms
32 Final model selection and evaluation
33 Next steps