Course Summary
This course offers an introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and neural networks. Students will learn how to apply these techniques to real-world problems.Key Learning Points
- Gain a solid understanding of machine learning concepts and techniques
- Learn how to apply machine learning algorithms to real-world problems
- Develop practical skills through hands-on programming assignments
Job Positions & Salaries of people who have taken this course might have
- Machine Learning Engineer
- USA: $112,000
- India: ₹1,200,000
- Spain: €48,000
- Data Scientist
- USA: $96,000
- India: ₹900,000
- Spain: €36,000
- Artificial Intelligence Researcher
- USA: $140,000
- India: ₹1,500,000
- Spain: €60,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of machine learning
- Be able to implement various machine learning algorithms
- Develop the skills to apply machine learning to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Deep Learning
- Applied Data Science with Python
Related Education Paths
Related Books
Description
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Outline
- Introduction
- Welcome to Machine Learning!
- Welcome
- What is Machine Learning?
- Supervised Learning
- Unsupervised Learning
- Machine Learning Honor Code
- What is Machine Learning?
- How to Use Discussion Forums
- Supervised Learning
- Unsupervised Learning
- Who are Mentors?
- Get to Know Your Classmates
- Frequently Asked Questions
- Lecture Slides
- Introduction
- Linear Regression with One Variable
- Model Representation
- Cost Function
- Cost Function - Intuition I
- Cost Function - Intuition II
- Gradient Descent
- Gradient Descent Intuition
- Gradient Descent For Linear Regression
- Model Representation
- Cost Function
- Cost Function - Intuition I
- Cost Function - Intuition II
- Gradient Descent
- Gradient Descent Intuition
- Gradient Descent For Linear Regression
- Lecture Slides
- Linear Regression with One Variable
- Linear Algebra Review
- Matrices and Vectors
- Addition and Scalar Multiplication
- Matrix Vector Multiplication
- Matrix Matrix Multiplication
- Matrix Multiplication Properties
- Inverse and Transpose
- Matrices and Vectors
- Addition and Scalar Multiplication
- Matrix Vector Multiplication
- Matrix Matrix Multiplication
- Matrix Multiplication Properties
- Inverse and Transpose
- Lecture Slides
- Linear Algebra
- Linear Regression with Multiple Variables
- Multiple Features
- Gradient Descent for Multiple Variables
- Gradient Descent in Practice I - Feature Scaling
- Gradient Descent in Practice II - Learning Rate
- Features and Polynomial Regression
- Normal Equation
- Normal Equation Noninvertibility
- Working on and Submitting Programming Assignments
- Setting Up Your Programming Assignment Environment
- Access to MATLAB Online and the Exercise Files for MATLAB Users
- Installing Octave on Windows
- Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)
- Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)
- Installing Octave on GNU/Linux
- More Octave/MATLAB resources
- Multiple Features
- Gradient Descent For Multiple Variables
- Gradient Descent in Practice I - Feature Scaling
- Gradient Descent in Practice II - Learning Rate
- Features and Polynomial Regression
- Normal Equation
- Normal Equation Noninvertibility
- Programming tips from Mentors
- Lecture Slides
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Basic Operations
- Moving Data Around
- Computing on Data
- Plotting Data
- Control Statements: for, while, if statement
- Vectorization
- Lecture Slides
- Please read if you've switched from the original version
- Octave/Matlab Tutorial
- Logistic Regression
- Classification
- Hypothesis Representation
- Decision Boundary
- Cost Function
- Simplified Cost Function and Gradient Descent
- Advanced Optimization
- Multiclass Classification: One-vs-all
- Classification
- Hypothesis Representation
- Decision Boundary
- Cost Function
- Simplified Cost Function and Gradient Descent
- Advanced Optimization
- Multiclass Classification: One-vs-all
- Lecture Slides
- Logistic Regression
- Regularization
- The Problem of Overfitting
- Cost Function
- Regularized Linear Regression
- Regularized Logistic Regression
- The Problem of Overfitting
- Cost Function
- Regularized Linear Regression
- Regularized Logistic Regression
- Lecture Slides
- Regularization
- Neural Networks: Representation
- Non-linear Hypotheses
- Neurons and the Brain
- Model Representation I
- Model Representation II
- Examples and Intuitions I
- Examples and Intuitions II
- Multiclass Classification
- Model Representation I
- Model Representation II
- Examples and Intuitions I
- Examples and Intuitions II
- Multiclass Classification
- Lecture Slides
- Neural Networks: Representation
- Neural Networks: Learning
- Cost Function
- Backpropagation Algorithm
- Backpropagation Intuition
- Implementation Note: Unrolling Parameters
- Gradient Checking
- Random Initialization
- Putting It Together
- Autonomous Driving
- Cost Function
- Backpropagation Algorithm
- Backpropagation Intuition
- Implementation Note: Unrolling Parameters
- Gradient Checking
- Random Initialization
- Putting It Together
- Lecture Slides
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Deciding What to Try Next
- Evaluating a Hypothesis
- Model Selection and Train/Validation/Test Sets
- Diagnosing Bias vs. Variance
- Regularization and Bias/Variance
- Learning Curves
- Deciding What to Do Next Revisited
- Evaluating a Hypothesis
- Model Selection and Train/Validation/Test Sets
- Diagnosing Bias vs. Variance
- Regularization and Bias/Variance
- Learning Curves
- Deciding What to do Next Revisited
- Lecture Slides
- Advice for Applying Machine Learning
- Machine Learning System Design
- Prioritizing What to Work On
- Error Analysis
- Error Metrics for Skewed Classes
- Trading Off Precision and Recall
- Data For Machine Learning
- Prioritizing What to Work On
- Error Analysis
- Lecture Slides
- Machine Learning System Design
- Support Vector Machines
- Optimization Objective
- Large Margin Intuition
- Mathematics Behind Large Margin Classification
- Kernels I
- Kernels II
- Using An SVM
- Lecture Slides
- Support Vector Machines
- Unsupervised Learning
- Unsupervised Learning: Introduction
- K-Means Algorithm
- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
- Lecture Slides
- Unsupervised Learning
- Dimensionality Reduction
- Motivation I: Data Compression
- Motivation II: Visualization
- Principal Component Analysis Problem Formulation
- Principal Component Analysis Algorithm
- Reconstruction from Compressed Representation
- Choosing the Number of Principal Components
- Advice for Applying PCA
- Lecture Slides
- Principal Component Analysis
- Anomaly Detection
- Problem Motivation
- Gaussian Distribution
- Algorithm
- Developing and Evaluating an Anomaly Detection System
- Anomaly Detection vs. Supervised Learning
- Choosing What Features to Use
- Multivariate Gaussian Distribution
- Anomaly Detection using the Multivariate Gaussian Distribution
- Lecture Slides
- Anomaly Detection
- Recommender Systems
- Problem Formulation
- Content Based Recommendations
- Collaborative Filtering
- Collaborative Filtering Algorithm
- Vectorization: Low Rank Matrix Factorization
- Implementational Detail: Mean Normalization
- Lecture Slides
- Recommender Systems
- Large Scale Machine Learning
- Learning With Large Datasets
- Stochastic Gradient Descent
- Mini-Batch Gradient Descent
- Stochastic Gradient Descent Convergence
- Online Learning
- Map Reduce and Data Parallelism
- Lecture Slides
- Large Scale Machine Learning
- Application Example: Photo OCR
- Problem Description and Pipeline
- Sliding Windows
- Getting Lots of Data and Artificial Data
- Ceiling Analysis: What Part of the Pipeline to Work on Next
- Summary and Thank You
- Lecture Slides
- Application: Photo OCR
Summary of User Reviews
Learn machine learning online with Coursera. This course has received rave reviews from students who have found it to be comprehensive, engaging, and practical. One key aspect that many users thought was good is the instructor's ability to explain complex concepts in a clear and concise way.Pros from User Reviews
- Comprehensive course material
- Engaging and practical learning experience
- Clear and concise explanations of complex concepts
- Flexible learning schedule
- Supportive online community
Cons from User Reviews
- High workload and time commitment
- Requires prior knowledge of programming and math
- Some assignments and quizzes can be challenging
- Limited opportunities for one-on-one interaction with the instructor
- Expensive compared to other online courses
Andrew NgTop Instructor
4.9 Raiting
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