Course Summary
The Art and Science of Machine Learning course provides an introduction to the key concepts and techniques used in machine learning, including supervised and unsupervised learning, clustering, and more.Key Learning Points
- Learn how to apply machine learning techniques to solve real-world problems
- Understand the principles behind popular machine learning algorithms
- Gain hands-on experience with Python programming and popular machine learning libraries
Related Topics for further study
Learning Outcomes
- Understand the principles and concepts behind popular machine learning algorithms
- Gain hands-on experience with Python programming and popular machine learning libraries
- Apply machine learning techniques to solve real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of programming concepts
- Familiarity with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-Paced
Similar Courses
- Applied Data Science with Python
- Deep Learning Specialization
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yoshua Bengio
Related Books
Description
Welcome to the Art and Science of machine learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
Knowledge
- Generalize a ML model using Regularization techniques
- Tune batch size and learning rate for better model performance
- Optimize a ML model
- Apply the concepts in TensorFlow code
Outline
- Introduction
- Course Introduction
- Getting Started with Google Cloud Platform and Qwiklabs
- The Art of ML
- Introduction
- Regularization
- L1 & L2 Regularizations
- Lab Intro: Regularization
- Lab: Regularization
- Learning rate and batch size
- Optimization
- Lab Intro: Reviewing Learning Curves
- Resources Readings - 1 - The Art of ML (The Art of ML)
- Resources Readings - 2 - The art of ML (Learning rate and batch size)
- The Art of ML: Regularization
- Hyperparameter Tuning
- Introduction
- Parameters vs Hyperparameters
- Think Beyond Grid Search
- Lab Intro: Export data from BigQuery to Google Cloud Storage
- Lab Intro: Performing Hyperparameter Tuning
- Resources Readings - 3 - Hyperparameter Tuning
- Hyperparameter Tuning
- A Pinch of Science
- Introduction
- Regularization for sparsity
- Lab: L1 Regularization
- Lab Solution: L1 Regularization
- Logistic Regression
- Resources Readings - 4 - A Pinch of Science (Regularization for sparsity)
- Resources Readings - 5 - A Pinch of Science (Logistic regression)
- L1 Regularization
- Logistic Regression
- The Science of Neural Networks
- Introduction to Neural Networks
- Neural Networks
- Lab: Neural Networks Playground
- Training Neural Networks
- Lab Intro: Build a DNN using the Keras Functional API
- Lab Intro: Training Models at Scale with AI Platform
- Multi-class Neural Networks
- Resources Readings - 6 - The Science of Neural Networks
- Training Neural Networks
- Multi-class Neural Networks
- Embeddings
- Introduction to Embeddings
- Review of Embeddings
- Recommendations
- Data-driven Embeddings
- Sparse Tensors
- Train an Embedding
- Similarity Property
- Lab Intro: Introducing the Functional API
- Resources Readings - 7 - Embedding
- Embeddings
- Summary
- Course Summary
- Resources - Compiled List of Readings
- All Quiz Questions as on PDF
- Course Slides
Summary of User Reviews
Discover the Art and Science of Machine Learning with this highly rated course on Coursera. Users rave about the comprehensive coverage of key concepts and practical applications of ML, with many finding the course highly engaging and informative.Key Aspect Users Liked About This Course
The course is highly comprehensive, covering key concepts and practical applications of ML.Pros from User Reviews
- Great coverage of key ML concepts
- Engaging and informative course structure
- Well-organized and easy to follow
- Excellent resources provided for further learning
- Practical assignments and exercises to reinforce learning
Cons from User Reviews
- Some users found the pace of the course too fast
- Requires a solid foundation in mathematics and programming
- Not suitable for complete beginners
- Some technical issues reported with the platform
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