**Applied Machine Learning: Algorithms**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 24m | 348 MB

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

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

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