When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Neural Networks and Random Forests

This course is part of AI for Scientific Research Specialization

Instructor: LearnQuest Network

5,062 already enrolled

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

18 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

18 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the AI for Scientific Research Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Module details

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.

What's included

5 videos3 readings2 assignments1 discussion prompt

5 videosTotal 15 minutes

Course Intro1 minute
Module Intro0 minutes
Neural Network Visualized6 minutes
Loss Functions4 minutes
Activation Functions4 minutes

3 readingsTotal 35 minutes

Neural Network Playground10 minutes
Backpropagation10 minutes
Common Activation Functions15 minutes

2 assignmentsTotal 40 minutes

Feed Forward and Backpropagation10 minutes
Neural Network Basics30 minutes

1 discussion promptTotal 10 minutes

Welcome to the Course!10 minutes

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.

What's included

3 videos5 readings1 assignment1 ungraded lab

3 videosTotal 8 minutes

Module Intro1 minute
Introduction to TensorFlow and Keras4 minutes
Deep Dive into Keras3 minutes

5 readingsTotal 70 minutes

Implementing a ML Model with Tensorflow and Keras20 minutes
Bias-Variance Tradeoff10 minutes
Configuring the Learning Rate10 minutes
Multi-Layer Networks10 minutes
Scientific Article: Neural Networks in Bioinformatics 20 minutes

1 assignmentTotal 30 minutes

Advanced Neural Networks30 minutes

1 ungraded labTotal 45 minutes

Coding Neural Networks45 minutes

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.