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Andrew Ng,
Eddy Shyu,
Aarti Bagul,
and
Geoff Ladwig
In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
Read more
In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
Read more
In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Register for this course and see more details by visiting:
OpenCourser.com/course/g1jq38/advanced
What's inside
Syllabus
Neural Networks
This week, you'll learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization).
Read more
Syllabus
Neural Networks
This week, you'll learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization).
Read more
Neural network training
This week, you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training. Finally, you will get a brief introduction to other layer types besides the one you've seen thus far.
Advice for applying machine learning
This week you'll learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data.
Decision trees
This week, you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost).
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines neural networks, a foundational concept in machine learning
Explores decision trees, a commonly used machine learning algorithm
Teaches students best practices for training and evaluating machine learning algorithms
Taught by Andrew Ng, an AI visionary and leader in the field
Emphasizes practical applications and industry best practices
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Advanced Learning Algorithms with these
activities:
Review basic probability and statistics concepts
Show steps
Strengthen your foundation in probability and statistics for better understanding of machine learning algorithms.
Browse courses on
Probability
Show steps
-
Go through notes or textbooks to refresh your knowledge of probability and statistical concepts.
-
Solve practice problems to test your understanding.
-
Review the mathematical foundations of machine learning algorithms.
Organize and summarize course materials
Show steps
Stay organized and improve retention by compiling and summarizing course materials.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Organize materials into logical categories, such as lecture notes, assignments, and resources.
-
Summarize key concepts and insights from the course materials.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques by reading this foundational book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the chapters relevant to the course topics.
-
Work through the code examples provided in the book.
-
Complete the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice building and training neural networks with TensorFlow
Show steps
Build and train neural networks to reinforce your understanding of their architecture and training process.
Browse courses on
Neural Networks
Show steps
-
Follow the guided tutorials to build a neural network using TensorFlow.
-
Train the neural network on a dataset provided by the course or find your own dataset.
-
Experiment with different neural network architectures and training parameters.
Participate in a study group with classmates
Show steps
Enhance your understanding and problem-solving skills through peer collaboration.
Show steps
-
Find a group of classmates with diverse backgrounds and perspectives.
-
Schedule regular study sessions to discuss course topics, work on assignments together, and clarify concepts.
-
Take turns presenting material and leading discussions.
Follow online tutorials on advanced neural network architectures
Show steps
Expand your knowledge of neural networks by exploring advanced architectures.
Browse courses on
Neural Networks
Show steps
-
Find reputable online courses or tutorials on advanced neural network architectures.
-
Follow the tutorials and implement the architectures in your own projects.
-
Experiment with different architectures to understand their strengths and weaknesses.
Create a presentation on the applications of decision trees
Show steps
Deepen your understanding of decision trees and their applications by creating a presentation.
Browse courses on
Decision Trees
Show steps
-
Research the different types of decision trees and their applications in various domains.
-
Choose a specific application area and gather data to build a decision tree model.
-
Create a presentation that explains the decision tree model, its training process, and its performance.
Build a machine learning model to predict customer churn
Show steps
Apply your machine learning skills to a real-world problem by developing a predictive model.
Browse courses on
Machine Learning Projects
Show steps
-
Gather a dataset on customer churn.
-
Clean and prepare the data for modeling.
-
Build and train a machine learning model to predict customer churn.
-
Evaluate the performance of the model and make improvements as needed.
-
Deploy the model to a production environment.
Review basic probability and statistics concepts
Show steps
Strengthen your foundation in probability and statistics for better understanding of machine learning algorithms.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Go through notes or textbooks to refresh your knowledge of probability and statistical concepts.
- Solve practice problems to test your understanding.
- Review the mathematical foundations of machine learning algorithms.
Organize and summarize course materials
Show steps
Stay organized and improve retention by compiling and summarizing course materials.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Organize materials into logical categories, such as lecture notes, assignments, and resources.
- Summarize key concepts and insights from the course materials.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques by reading this foundational book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to the course topics.
- Work through the code examples provided in the book.
- Complete the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice building and training neural networks with TensorFlow
Show steps
Build and train neural networks to reinforce your understanding of their architecture and training process.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Follow the guided tutorials to build a neural network using TensorFlow.
- Train the neural network on a dataset provided by the course or find your own dataset.
- Experiment with different neural network architectures and training parameters.
Participate in a study group with classmates
Show steps
Enhance your understanding and problem-solving skills through peer collaboration.
Show steps
Show steps
Show steps
- Find a group of classmates with diverse backgrounds and perspectives.
- Schedule regular study sessions to discuss course topics, work on assignments together, and clarify concepts.
- Take turns presenting material and leading discussions.
Follow online tutorials on advanced neural network architectures
Show steps
Expand your knowledge of neural networks by exploring advanced architectures.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find reputable online courses or tutorials on advanced neural network architectures.
- Follow the tutorials and implement the architectures in your own projects.
- Experiment with different architectures to understand their strengths and weaknesses.
Create a presentation on the applications of decision trees
Show steps
Deepen your understanding of decision trees and their applications by creating a presentation.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Research the different types of decision trees and their applications in various domains.
- Choose a specific application area and gather data to build a decision tree model.
- Create a presentation that explains the decision tree model, its training process, and its performance.
Build a machine learning model to predict customer churn
Show steps
Apply your machine learning skills to a real-world problem by developing a predictive model.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Gather a dataset on customer churn.
- Clean and prepare the data for modeling.
- Build and train a machine learning model to predict customer churn.
- Evaluate the performance of the model and make improvements as needed.
- Deploy the model to a production environment.
Career center
Learners who complete Advanced Learning Algorithms will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers research and build machine learning models that can perform tasks like object recognition and text translation. This course could help you prepare for this role, particularly with its emphasis on building and training neural networks.
Researcher
Researcher
Researchers conduct research in a variety of fields. This course could be useful for Researchers interested in conducting research in machine learning, as it covers topics such as neural networks and decision trees.
Professor
Professor
Professors teach and conduct research at universities. This course could be useful for Professors teaching machine learning, as it covers topics such as neural networks and decision trees.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course could be useful for Quantitative Analysts interested in using machine learning to analyze financial data, as it covers topics such as neural networks and decision trees.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course could be useful for Actuaries interested in using machine learning to assess risk and uncertainty, as it covers topics such as neural networks and decision trees.
Data Scientist
Data Scientist
Data Scientists analyze data to find trends and patterns that lead to valuable insights. This course could help you get started in data science by giving you the skills you need to build and train machine learning models. Learning more about neural networks and decision trees could prove particularly valuable for this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve the efficiency of systems. This course could be useful for Operations Research Analysts interested in using machine learning to improve the efficiency of systems, as it covers topics such as neural networks and decision trees.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make recommendations to investors. This course could be useful for Financial Analysts interested in using machine learning to analyze financial data, as it covers topics such as neural networks and decision trees.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to provide insights to businesses. This course could be helpful for Data Analysts interested in using machine learning to analyze data, as it covers topics such as neural networks and decision trees.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course could be useful for Data Engineers interested in working with machine learning data, as it covers topics such as neural networks and decision trees.
Software Engineer
Software Engineer
Software Engineers design, code, and test software systems. This course could prove useful for Software Engineers working in machine learning, as it covers essential topics such as neural networks, decision trees, and machine learning best practices.
Market Research Analyst
Market Research Analyst
Market Research Analysts study market trends and consumer behavior to help businesses make better decisions. This course could be useful for Market Research Analysts interested in using machine learning to analyze market data, as it covers topics such as neural networks and decision trees.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to improve performance. This course could be useful for Business Analysts interested in using machine learning to solve business problems, as it covers topics such as neural networks and decision trees.
Consultant
Consultant
Consultants provide advice and solutions to businesses on a variety of topics. This course could be useful for Consultants interested in providing advice on machine learning, as it covers topics such as neural networks and decision trees.
Product Manager
Product Manager
Product Managers lead the development of products from concept to launch. This course could be helpful for Product Managers working on machine learning products, as it could help them better understand the algorithms and techniques used to build them.
For more career information including salaries, visit:
OpenCourser.com/course/g1jq38/advanced
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced Learning Algorithms.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, algorithms, and applications of this field. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of deep learning.
Provides a hands-on introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for beginners who want to learn how to build and train machine learning models.
Provides a comprehensive introduction to machine learning, covering the theoretical foundations, algorithms, and applications of this field. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of machine learning.
Provides a probabilistic perspective on machine learning, covering the theoretical foundations, algorithms, and applications of this field. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering the theoretical foundations, algorithms, and applications of these fields. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of pattern recognition and machine learning.
Provides a practical introduction to machine learning, covering the essential concepts and techniques needed to build effective machine learning models. It is written by Peter Harrington, one of the leading experts in the field of machine learning.
Provides a comprehensive overview of statistical learning, covering the theoretical foundations, algorithms, and applications of this field. It valuable resource for both beginners and experienced practitioners who want to deepen their understanding of statistical learning.
Save
Provides a practical introduction to data mining, covering the essential concepts and techniques needed to build effective data mining models. It is written by Ian Witten, Eibe Frank, and Mark Hall, leading experts in the field of data mining.
Provides a practical introduction to machine learning using the R programming language. It is written by Brett Lantz, a leading expert in the field of machine learning.
Provides a practical introduction to machine learning for people with no prior experience in the field. It is written by Drew Conway and John Myles White, leading experts in the field of machine learning.
Provides a practical introduction to machine learning using the Python programming language. It is written by Sebastian Raschka and Vahid Mirjalili, leading experts in the field of machine learning.
Save
Provides a practical introduction to machine learning for business people. It is written by Pete Warden, a leading expert in the field of machine learning.
Save
Provides a practical introduction to machine learning for lawyers. It is written by Monica Bay, a leading expert in the field of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/g1jq38/advanced
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
Level
Introductory
Via
Coursera
Institutions
Stanford University
DeepLearning.AI
Instructors
Andrew Ng
Eddy Shyu
Aarti Bagul
Geoff Ladwig
Language
English
Transcripts
Español
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning. It's comprised of three courses:
- Advanced Learning Algorithms (viewing)
- Supervised Machine Learning: Regression and Classification
- Unsupervised Learning, Recommenders, Reinforcement Learning
Good to know
Examines neural networks, a foundational concept in machine learning
Explores decision trees, a commonly used machine learning algorithm
Teaches students best practices for training and evaluating machine learning algorithms
Taught by Andrew Ng, an AI visionary and leader in the field
Emphasizes practical applications and industry best practices
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