Course Summary
This is a comprehensive course on Machine Learning that covers various topics such as supervised learning, unsupervised learning, and deep learning. It also includes hands-on assignments to help students gain practical experience.Key Learning Points
- The course covers both theoretical and practical aspects of Machine Learning
- Students will learn to implement Machine Learning algorithms using Python
- The course is taught by experienced instructors from Duke University
Job Positions & Salaries of people who have taken this course might have
- USA: $112,000
- India: ₹1,100,000
- Spain: €44,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €44,000
- USA: $120,000
- India: ₹1,500,000
- Spain: €50,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €44,000
- USA: $120,000
- India: ₹1,500,000
- Spain: €50,000
- USA: $150,000
- India: ₹2,000,000
- Spain: €60,000
Related Topics for further study
Learning Outcomes
- Understand the fundamental concepts of Machine Learning
- Implement Machine Learning algorithms using Python
- Apply Machine Learning techniques to solve real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Understanding of linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures with quizzes and assignments
Similar Courses
- Applied Data Science with Python
- Neural Networks and Deep Learning
- Python for Everybody
Related Education Paths
- Machine Learning Engineer Certification
- Data Scientist Certification
- Artificial Intelligence Certification
Related Books
Description
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Outline
- Simple Introduction to Machine Learning
- Why Machine Learning Is Exciting
- What Is Machine Learning?
- Logistic Regression
- Interpretation of Logistic Regression
- Motivation for Multilayer Perceptron
- Multilayer Perceptron Concepts
- Multilayer Perceptron Math Model
- Deep Learning
- Example: Document Analysis
- Interpretation of Multilayer Perceptron
- Transfer Learning
- Model Selection
- Early History of Neural Networks
- Hierarchical Structure of Images
- Convolution Filters
- Convolutional Neural Network
- CNN Math Model
- How the Model Learns
- Advantages of Hierarchical Features
- CNN on Real Images
- Applications in Use and Practice
- Deep Learning and Transfer Learning
- Introduction to PyTorch
- Course Information
- Math for Data Science
- Intro to Machine Learning
- Logistic Regression
- Multilayer Perceptron
- Deep Learning
- Model Selection
- History of Neural Networks
- CNN Concepts
- CNN Math Model
- Basics of Model Learning
- How Do We Define Learning?
- How Do We Evaluate Our Networks?
- How Do We Learn Our Network?
- How Do We Handle Big Data?
- Early Stopping
- Model Learning with PyTorch
- Lesson One
- Lesson 2
- Image Analysis with Convolutional Neural Networks
- Motivation: Diabetic Retinopathy
- Breakdown of the Convolution (1D and 2D)
- Core Components of the Convolutional Layer
- Activation Functions
- Pooling and Fully Connected Layers
- Training the Network
- Transfer Learning and Fine-Tuning
- CNN with PyTorch
- Lesson One
- Lesson 2
- Lesson 3
- Recurrent Neural Networks for Natural Language Processing
- Introduction to the Concept of Word Vectors
- Words to Vectors
- Example of Word Embeddings
- Neural Model of Text
- The Softmax Function
- Methods for Learning Model Parameters
- More Details on How to Learn Model Parameters
- The Recurrent Neural Network
- Long Short-Term Memory
- Long Short-Term Memory Review
- Use of LSTM for Text Synthesis
- Simple and Effective Alternative Methods for Neural NLP
- Natural Language Processing with PyTorch
- Lesson 1
- Lesson 2
- Lesson 3
- Week 4 Comprehensive
- The Transformer Network for Natural Language Processing
- Word Vectors and Their Interpretation
- Relationships Between Word Vectors
- Inner Products Between Word Vectors
- Intuition Into Meaning of Inner Products of Word Vectors
- Introduction of Attention MechanismÂ
- Queries, Keys, and Values of Attention Network
- Self-Attention and Positional Encodings
- Attention-Based Sequence Encoder
- Coupling the Sequence Encoder and DecoderÂ
- Cross Attention in the Sequence-to-Sequence Model
- Multi-Head Attention
- The Complete Transformer Network
- Introduction to Reinforcement Learning
- Introduction to Reinforcement Learning
- Reinforcement Learning Problem Setup
- Example of Reinforcement Learning in Practice
- Reinforcement Learning with PyTorch
- Moving to a Non-Myopic Policy
- Q Learning
- Extensions of Q Learning
- Limitations of Q Learning, and Introduction to Deep Q Learning
- Deep Q Learning Based on Images
- Connecting Deep Q Learning with Conventional Q Learning
Summary of User Reviews
Discover the powerful world of machine learning with Duke University's online course on Coursera. Students rave about the engaging, easy-to-follow lectures and practical assignments. One key aspect that users found particularly helpful was the emphasis on real-world applications of machine learning principles.Pros from User Reviews
- Engaging and easy-to-follow lectures
- Practical assignments
- Real-world applications of machine learning principles
- Instructors are knowledgeable and responsive
- Course covers a broad range of topics
Cons from User Reviews
- Some students may find the course challenging
- Not suitable for beginners with no programming experience
- Course materials can be overwhelming
- Limited interaction with other students
- May require additional resources to fully understand some topics
Lawrence Carin
4.7 Raiting
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