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Machine Learning: Classification

Approx. 21 hours to complete

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Course Summary

This course focuses on classification in machine learning, covering various algorithms and techniques used to classify data. The course provides hands-on experience with real-world datasets and practical applications of classification techniques.

Key Learning Points

Learn about different classification algorithms and their applications
Gain practical experience through hands-on projects with real-world datasets
Understand the importance of data preprocessing and feature selection in classification

Learning Outcomes

Ability to apply various classification algorithms to real-world datasets
Understanding of the importance of data preprocessing and feature selection
Hands-on experience with practical applications of machine learning classification techniques

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming and statistics
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Data Science with Python
Machine Learning
Data Mining

Related Education Paths

Notable People in This Field

Professor of Computer Science, New York University
Professor, University of Toronto; Chief Scientific Adviser, Vector Institute
Professor of Computer Science, Stanford University; Co-director, Stanford Human-Centered AI Institute

Related Books

Description

Case Studies: Analyzing Sentiment & Loan Default Prediction

In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

Outline

Welcome!
Welcome to the classification course, a part of the Machine Learning Specialization
What is this course about?
Impact of classification
Course overview
Outline of first half of course
Outline of second half of course
Assumed background
Let's get started!
Important Update regarding the Machine Learning Specialization
Slides presented in this module
Reading: Software tools you'll need

Linear Classifiers & Logistic Regression
Linear classifiers: A motivating example
Intuition behind linear classifiers
Decision boundaries
Linear classifier model
Effect of coefficient values on decision boundary
Using features of the inputs
Predicting class probabilities
Review of basics of probabilities
Review of basics of conditional probabilities
Using probabilities in classification
Predicting class probabilities with (generalized) linear models
The sigmoid (or logistic) link function
Logistic regression model
Effect of coefficient values on predicted probabilities
Overview of learning logistic regression models
Encoding categorical inputs
Multiclass classification with 1 versus all
Recap of logistic regression classifier
Slides presented in this module
Predicting sentiment from product reviews
Linear Classifiers & Logistic Regression
Predicting sentiment from product reviews

Learning Linear Classifiers
Goal: Learning parameters of logistic regression
Intuition behind maximum likelihood estimation
Data likelihood
Finding best linear classifier with gradient ascent
Review of gradient ascent
Learning algorithm for logistic regression
Example of computing derivative for logistic regression
Interpreting derivative for logistic regression
Summary of gradient ascent for logistic regression
Choosing step size
Careful with step sizes that are too large
Rule of thumb for choosing step size
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick
(VERY OPTIONAL) Expressing the log-likelihood
(VERY OPTIONAL) Deriving probability y=-1 given x
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form
(VERY OPTIONAL) Deriving gradient of log likelihood
Recap of learning logistic regression classifiers
Slides presented in this module
Implementing logistic regression from scratch
Learning Linear Classifiers
Implementing logistic regression from scratch

Overfitting & Regularization in Logistic Regression
Evaluating a classifier
Review of overfitting in regression
Overfitting in classification
Visualizing overfitting with high-degree polynomial features
Overfitting in classifiers leads to overconfident predictions
Visualizing overconfident predictions
(OPTIONAL) Another perspecting on overfitting in logistic regression
Penalizing large coefficients to mitigate overfitting
L2 regularized logistic regression
Visualizing effect of L2 regularization in logistic regression
Learning L2 regularized logistic regression with gradient ascent
Sparse logistic regression with L1 regularization
Recap of overfitting & regularization in logistic regression
Slides presented in this module
Logistic Regression with L2 regularization
Overfitting & Regularization in Logistic Regression
Logistic Regression with L2 regularization

Decision Trees
Predicting loan defaults with decision trees
Intuition behind decision trees
Task of learning decision trees from data
Recursive greedy algorithm
Learning a decision stump
Selecting best feature to split on
When to stop recursing
Making predictions with decision trees
Multiclass classification with decision trees
Threshold splits for continuous inputs
(OPTIONAL) Picking the best threshold to split on
Visualizing decision boundaries
Recap of decision trees
Slides presented in this module
Identifying safe loans with decision trees
Implementing binary decision trees
Decision Trees
Identifying safe loans with decision trees
Implementing binary decision trees

Preventing Overfitting in Decision Trees
A review of overfitting
Overfitting in decision trees
Principle of Occam's razor: Learning simpler decision trees
Early stopping in learning decision trees
(OPTIONAL) Motivating pruning
(OPTIONAL) Pruning decision trees to avoid overfitting
(OPTIONAL) Tree pruning algorithm
Recap of overfitting and regularization in decision trees
Slides presented in this module
Decision Trees in Practice
Preventing Overfitting in Decision Trees
Decision Trees in Practice

Handling Missing Data
Challenge of missing data
Strategy 1: Purification by skipping missing data
Strategy 2: Purification by imputing missing data
Modifying decision trees to handle missing data
Feature split selection with missing data
Recap of handling missing data
Slides presented in this module
Handling Missing Data

Boosting
The boosting question
Ensemble classifiers
Boosting
AdaBoost overview
Weighted error
Computing coefficient of each ensemble component
Reweighing data to focus on mistakes
Normalizing weights
Example of AdaBoost in action
Learning boosted decision stumps with AdaBoost
The Boosting Theorem
Overfitting in boosting
Ensemble methods, impact of boosting & quick recap
Slides presented in this module
Exploring Ensemble Methods
Boosting a decision stump
Exploring Ensemble Methods
Boosting
Boosting a decision stump

Precision-Recall
Case-study where accuracy is not best metric for classification
What is good performance for a classifier?
Precision: Fraction of positive predictions that are actually positive
Recall: Fraction of positive data predicted to be positive
Precision-recall extremes
Trading off precision and recall
Precision-recall curve
Recap of precision-recall
Slides presented in this module
Exploring precision and recall
Precision-Recall
Exploring precision and recall

Scaling to Huge Datasets & Online Learning
Gradient ascent won't scale to today's huge datasets
Timeline of scalable machine learning & stochastic gradient
Why gradient ascent won't scale
Stochastic gradient: Learning one data point at a time
Comparing gradient to stochastic gradient
Why would stochastic gradient ever work?
Convergence paths
Shuffle data before running stochastic gradient
Choosing step size
Don't trust last coefficients
(OPTIONAL) Learning from batches of data
(OPTIONAL) Measuring convergence
(OPTIONAL) Adding regularization
The online learning task
Using stochastic gradient for online learning
Scaling to huge datasets through parallelization & module recap
Slides presented in this module
Training Logistic Regression via Stochastic Gradient Ascent
Scaling to Huge Datasets & Online Learning
Training Logistic Regression via Stochastic Gradient Ascent

Summary of User Reviews

The ML Classification course on Coursera has received positive reviews from students. The course is highly recommended and provides a comprehensive understanding of machine learning classification. Many users have praised the hands-on approach and practical exercises, which help reinforce the concepts learned in the course.

Key Aspect Users Liked About This Course

The hands-on approach and practical exercises are highly praised by many users.

Pros from User Reviews

Comprehensive understanding of machine learning classification
Practical exercises and hands-on approach
Clear and concise explanations of complex topics
Engaging and knowledgeable instructors
Flexible schedule and self-paced learning

Cons from User Reviews

Some users found the course challenging and difficult to follow
Course materials can be overwhelming at times
Lack of personalized feedback from instructors
Limited interaction with other students
Not suitable for beginners or those with no prior knowledge of machine learning

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