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This course introduces common classification techniques and algorithms in order to predict the value of the class label based on the features in the dataset. We will discuss four different classification algorithms in detail, namely, logistic regression, k-nearest neighbors, support vector machines, and decision trees. For each algorithm, we will cover the theory behind how it works, then demonstrate an implementation of it in Python. By the end of this course, you will be able to train your own classifiers with confidence.
Duration
4 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Angela Zhao
This course is composed by the following modules
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Module 1
Introduction1 video
Introduction to the Classification Algorithms
Module 2
Logistic Regression5 videos
Binary Classification with Logistic Regression
Logistic Regression Hypothesis Function
Logistic Regression Cost Function
Gradient Descent
Multiclass Classification with Logistic Regression
Module 3
KNN3 videos
K-Nearest Neighbor Algorithm
Choosing the K for KNN
KNN Distance Metrics
Module 4
SVM2 videos
Linear Classification with Support Vector Machines
Nonlinear Classification with Support Vector Machines
Module 5
Decision Trees1 video
Decision Trees
Module 6
Using Scikit-Learn Models1 video
Using Scikit-Learn Models
Module 7
Model Evaluation1 video
Evaluating Machine Learning Models
Module 8
Project1 video, 1 lab
Solution: Classifying Penguins with Machine Learning
Classifying Penguins with Machine Learning
Module 9
Conclusion1 video
Course Conclusion
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