Brief Introduction
You will learn about and practice a variety of Supervised, Unsupervised and Reinforcement Learning approaches. Supervised Learning is an important component of all kinds of technologies, from stopping credit card fraud, to finding faces in camera images, to recognizing spoken language. Our goal is to give you the skills that you need to understand these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising. ClCourse Summary
This course covers the fundamentals of machine learning, including supervised and unsupervised learning, as well as neural networks and deep learning.Key Learning Points
- Learn how to use Scikit-Learn and TensorFlow to implement machine learning models
- Understand the different types of machine learning algorithms and when to use them
- Explore real-world applications of machine learning, including image and speech recognition
Related Topics for further study
Learning Outcomes
- Ability to implement machine learning models using Scikit-Learn and TensorFlow
- Understanding of the different types of machine learning algorithms and when to use them
- Experience with real-world applications of machine learning
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
Similar Courses
- Deep Learning
- Artificial Intelligence for Trading
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
In this course, you'll learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques for solving a range of data science problems.Requirements
- A strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding of Intro to Statistics , especially Lessons 8, 9 and 10 , would be helpful. Students should also have some experience in programming (perhaps through Introduction to CS ) and a familiarity with Neural Networks (as covered in Introduction to Artificial Intelligence ). See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Supervised Learning Machine Learning is the ROX Decision Trees Regression and Classification Neural Networks Instance-Based Learning Ensemble B&B Kernel Methods and Support Vector Machines (SVM)s Computational Learning Theory VC Dimensions Bayesian Learning Bayesian Inference lesson 2 Unsupervised Learning Randomized optimization Clustering Feature Selection Feature Transformation Information Theory lesson 3 Reinforcement Learning Markov Decision Processes Reinforcement Learning Game Theory
Summary of User Reviews
The machine learning course on Udacity is highly rated by users. It is designed to teach the fundamental concepts of machine learning and how to apply them in real-world scenarios. Many users appreciated the hands-on approach and practical exercises provided in the course.Key Aspect Users Liked About This Course
Hands-on approach and practical exercisesPros from User Reviews
- Well-structured and easy to follow curriculum
- Excellent instructors with industry experience
- Real-world projects and case studies
- Interactive quizzes and assignments for better understanding
- Flexible schedule and self-paced learning
Cons from User Reviews
- Some users found the course to be too basic for their level of expertise
- Limited interaction with instructors and other students
- Does not cover advanced topics in machine learning
- No official certification or accreditation
- Requires a basic understanding of programming and statistics
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