English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 58m | 268 MB
With the growing importance of machine learning in almost every sector, professionals need a deeper understanding and practical approach to implementing ML algorithms effectively.
This course covers commonly used machine learning algorithms. Instructor Matt Harrison focuses on non-deep learning algorithms, covering PCA, clustering, linear and logistic regression, decision trees, random forests, and gradient boosting.
Join Matt in this course to understand common ML algorithms, learn their pros and cons, and develop hands-on skills to leverage them by following along with challenges and solutions in GitHub Codespaces.
Table of Contents
Introduction
1 Applied machine learning Algorithms
2 What you should know
Clustering
3 K-means
4 K evaluation
5 Understanding clusters
6 Other algorithms
7 Challenge Apply KNN
8 Solution Apply KNN
PCA
9 PCA
10 Structure of components
11 Components
12 Scatter plot
13 Other algorithms
14 Challenge Utilize PCA
15 Solution Utilize PCA
Linear Regression
16 Linear regression algorithm
17 scikit-learn
18 Real-world example
19 Assumptions
20 Challenge Develop a linear regression model
21 Solution Develop a linear regression model
Logistic Regression
22 Logistic regression algorithm
23 Basic example
24 Assumptions
25 Challenge Construct a logistic regression model
26 Solution Construct a logistic regression model
Decision Trees
27 Decision tree algorithm
28 Real-world example
29 Random Forest and XGBoost
30 Challenge Design a decision tree model
31 Solution Design a decision tree model
Conclusion
32 Next steps
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