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Rajvir Dua
and
Neelesh Tiruviluamala
In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.
Register for this course and see more details by visiting:
OpenCourser.com/course/djufn8/neural
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What's inside
Syllabus
Introduction to Neural Networks
In this module, we'll go through neural networks and how to use them in Python. We'll start by describing what a neural network is and how to construct one by combining a sequence of linear models. Then, we'll talk about converge of neural networks in the hopes of minimizing a loss function. Finally, we'll learn how to code a neural network in Python.
Read more
Syllabus
Introduction to Neural Networks
In this module, we'll go through neural networks and how to use them in Python. We'll start by describing what a neural network is and how to construct one by combining a sequence of linear models. Then, we'll talk about converge of neural networks in the hopes of minimizing a loss function. Finally, we'll learn how to code a neural network in Python.
Read more
Deep Dive into Neural Networks
In this module, we'll take a more detailed look into neural network and the considerations we should be having when using them. We'll start by adding layers to our 2-layer network, exploring the different options and their effects. Then, we'll explore some more advanced Python libraries for neural networks in TensorFlow and Keras. Finally, we'll discuss the implications to science and how to apply the models in the space.
Exploring Random Forests
In this module, we'll build up our knowledge of random forests and their uses in science. We'll start by exploring decision trees and how they operate as models in isolation. Next, we'll look at the impact of combining decision trees to create random forests. From here, we'll talk about the similarities and differences between regression and classification with random forests before concluding with a final project predicting species from lineage.
Final Project: Comparing Models to Predict Sepal Width
In this final project, we'll be comparing a suite of models to find the one that best predicts sepal width.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines neural networks and random forests, which are standard in data science
Taught by Rajvir Dua and Neelesh Tiruviluamala, who are recognized for their work in machine learning
Develops neural networks and random forests, which are core skills for data science
Includes a mix of videos, readings, and discussions
Builds a strong foundation for beginners in neural networks and random forests
Teaches neural networks and random forests in Python, which is highly relevant to industry
Save this course
Save Neural Networks and Random Forests to your list so you can find it easily later:
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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 Neural Networks and Random Forests with these
activities:
Find a mentor who can provide guidance on neural networks
Show steps
Finding a mentor who can provide guidance on neural networks will help you to learn more about the field and advance your career.
Browse courses on
Neural Networks
Show steps
-
Identify potential mentors.
-
Reach out to potential mentors and ask for their guidance.
Review notes and assignments from past courses related to neural networks
Show steps
Reviewing notes and assignments from past courses will help you to refresh your knowledge of neural networks.
Browse courses on
Neural Networks
Show steps
-
Gather notes and assignments from past courses related to neural networks.
-
Review the materials.
-
Identify any areas where you need additional support.
Read and summarize 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
This book provides a comprehensive overview of deep learning and will help you to build a strong foundation for this course.
View
Deep Learning
on Amazon
Show steps
-
Read the book carefully and take notes.
-
Summarize the main points of each chapter.
-
Complete the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Code neural networks in Python
Show steps
Coding neural networks in Python will help you to develop a deeper understanding of how they work.
Browse courses on
Neural Networks
Show steps
-
Find a tutorial on how to code neural networks in Python.
-
Follow the tutorial and code the neural networks yourself.
-
Experiment with different neural network architectures and hyperparameters.
Join a study group and discuss neural networks
Show steps
Discussing neural networks with other students will help you to learn from their perspectives and deepen your understanding.
Browse courses on
Neural Networks
Show steps
-
Join a study group or form your own.
-
Discuss the course material.
-
Brainstorm ideas for projects.
Follow tutorials on advanced neural network topics
Show steps
Following tutorials on advanced neural network topics will help you to expand your knowledge and skills.
Browse courses on
Neural Networks
Show steps
-
Identify tutorials on advanced neural network topics.
-
Follow the tutorials and complete the exercises.
Write a blog post about neural networks
Show steps
Writing a blog post about neural networks will help you to organize your thoughts and communicate your understanding to others.
Browse courses on
Neural Networks
Show steps
-
Choose a topic for your blog post.
-
Research the topic and write a draft.
-
Edit and publish your blog post.
Give a presentation on your neural network project
Show steps
Giving a presentation on your neural network project will help you to communicate your findings and demonstrate your understanding of the material.
Browse courses on
Neural Networks
Show steps
-
Prepare your presentation.
-
Practice your presentation.
-
Deliver your presentation.
Find a mentor who can provide guidance on neural networks
Show steps
Finding a mentor who can provide guidance on neural networks will help you to learn more about the field and advance your career.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify potential mentors.
- Reach out to potential mentors and ask for their guidance.
Review notes and assignments from past courses related to neural networks
Show steps
Reviewing notes and assignments from past courses will help you to refresh your knowledge of neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Gather notes and assignments from past courses related to neural networks.
- Review the materials.
- Identify any areas where you need additional support.
Read and summarize 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
This book provides a comprehensive overview of deep learning and will help you to build a strong foundation for this course.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book carefully and take notes.
- Summarize the main points of each chapter.
- Complete the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Code neural networks in Python
Show steps
Coding neural networks in Python will help you to develop a deeper understanding of how they work.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a tutorial on how to code neural networks in Python.
- Follow the tutorial and code the neural networks yourself.
- Experiment with different neural network architectures and hyperparameters.
Join a study group and discuss neural networks
Show steps
Discussing neural networks with other students will help you to learn from their perspectives and deepen your understanding.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Join a study group or form your own.
- Discuss the course material.
- Brainstorm ideas for projects.
Follow tutorials on advanced neural network topics
Show steps
Following tutorials on advanced neural network topics will help you to expand your knowledge and skills.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify tutorials on advanced neural network topics.
- Follow the tutorials and complete the exercises.
Write a blog post about neural networks
Show steps
Writing a blog post about neural networks will help you to organize your thoughts and communicate your understanding to others.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a topic for your blog post.
- Research the topic and write a draft.
- Edit and publish your blog post.
Give a presentation on your neural network project
Show steps
Giving a presentation on your neural network project will help you to communicate your findings and demonstrate your understanding of the material.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Prepare your presentation.
- Practice your presentation.
- Deliver your presentation.
Career center
Learners who complete Neural Networks and Random Forests will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist develops algorithms and models using statistics, machine learning, and programming. Neural Networks and Random Forests is a course that teaches the foundations of these critical skills, making it a valuable asset for anyone looking to enter the field of Data Science. The course's focus on Python programming and practical project experience will provide a strong foundation for success in this rapidly growing field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. Neural Networks and Random Forests provides a comprehensive introduction to these topics, covering the theoretical foundations as well as practical implementation in Python. The course's emphasis on hands-on project work will give learners the skills they need to succeed in this exciting field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Neural Networks and Random Forests provides a strong foundation in the principles of machine learning and artificial intelligence, which are becoming increasingly important in software development. The course's focus on Python programming and practical project experience will give learners the skills they need to succeed in this dynamic field.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this dynamic field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this challenging field.
Financial Analyst
Financial Analyst
Financial Analysts use data and analysis to make investment decisions. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this competitive field.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this challenging field.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks to businesses and organizations. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Neural Networks and Random Forests provides a strong foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this competitive field.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of medicine and public health. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Data Engineer
Data Engineer
Data Engineers design and build systems to manage and process data. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from cyberattacks. Neural Networks and Random Forests provides a solid foundation in the statistical and programming skills needed for this role. The course's emphasis on practical project work will give learners the hands-on experience they need to succeed in this growing field.
For more career information including salaries, visit:
OpenCourser.com/course/djufn8/neural
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
Neural Networks and Random Forests.
Save
Provides a comprehensive overview of deep learning, covering the latest advancements in the field. It valuable resource for students and researchers who want to learn more about deep learning.
Comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers who want to learn more about these topics.
Provides a probabilistic perspective on machine learning. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning. It valuable resource for students and researchers who want to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of neural networks and deep learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers who want to learn more about these topics.
Classic textbook on statistical learning. It covers a wide range of topics, including linear models, regression, and classification. It valuable resource for students and practitioners who want to learn more about the foundations of machine learning.
Provides a comprehensive overview of machine learning using R. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers who want to learn more about these topics.
Provides a comprehensive overview of machine learning in finance. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and practitioners who want to learn more about these topics.
Provides a practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a practical introduction to Bayesian statistics. It covers a wide range of topics, including Bayesian inference, graphical models, and hierarchical models. It valuable resource for students and practitioners who want to learn more about Bayesian statistics.
Practical guide to machine learning for hackers. It covers a wide range of topics, including data preprocessing, feature engineering, and model selection. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a practical guide to deep learning using Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for students and practitioners who want to learn how to apply deep learning to real-world problems.
Provides a practical guide to machine learning using R. It covers a wide range of topics, including data preprocessing, feature engineering, and model selection. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Gentle introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It good resource for students and practitioners who want to learn more about these topics without getting bogged down in the mathematics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/djufn8/neural
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Effort
3 weeks of study, 3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructors
Rajvir Dua
Neelesh Tiruviluamala
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
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This course belongs to a
collection
called
AI for Scientific Research. It's comprised of four courses:
- Capstone Project: Advanced AI for Drug Discovery
- Introduction to Data Science and scikit-learn in Python
- Neural Networks and Random Forests (viewing)
- Machine Learning Models in Science
Good to know
Examines neural networks and random forests, which are standard in data science
Taught by Rajvir Dua and Neelesh Tiruviluamala, who are recognized for their work in machine learning
Develops neural networks and random forests, which are core skills for data science
Includes a mix of videos, readings, and discussions
Builds a strong foundation for beginners in neural networks and random forests
Teaches neural networks and random forests in Python, which is highly relevant to industry
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