Neural Networks and Random Forests
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 10 hours
- Course by LearnQuest
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Offered by
About
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.Modules
Course Introduction
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2
Videos
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1
Discussion
1
Discussions
- Welcome to the Course!
2
Videos
- Course Intro
- Module Intro
What is a Neural Network?
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1
Videos
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1
Readings
1
Videos
- Neural Network Visualized
1
Readings
- Neural Network Playground
Loss Functions and Optimization
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1
Videos
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1
Readings
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1
Assignment
1
Assignment
- Feed Forward and Backpropagation
1
Videos
- Loss Functions
1
Readings
- Backpropagation
Activation Functions
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1
Videos
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1
Readings
1
Videos
- Activation Functions
1
Readings
- Common Activation Functions
Quiz: Neural Network Basics
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1
Assignment
1
Assignment
- Neural Network Basics
Introduction to TensorFlow and Keras
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2
Videos
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1
Readings
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1
Lab
1
Labs
- Coding Neural Networks
2
Videos
- Module Intro
- Introduction to TensorFlow and Keras
1
Readings
- Implementing a ML Model with Tensorflow and Keras
Bias-Variance Tradeoff (Over and Under-fitting)
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2
Readings
2
Readings
- Bias-Variance Tradeoff
- Configuring the Learning Rate
Deep Dive into Keras
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1
Videos
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2
Readings
1
Videos
- Deep Dive into Keras
2
Readings
- Multi-Layer Networks
- Scientific Article: Neural Networks in Bioinformatics
Quiz: Advanced Neural Networks
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1
Assignment
1
Assignment
- Advanced Neural Networks
Decision Trees Revisited
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2
Videos
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1
Discussion
1
Discussions
- Constructing Random Forests from Decision Trees
2
Videos
- Module Intro
- Exploring Trees
Random Forest Classification and Regression
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2
Readings
2
Readings
- Scitkit-learn docs: Random Forest Classifier
- Scikit-learn Docs: Random Forest Regressor
Programming Assignment: Predicting Species of Animal based on Lineage
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1
Programming
- Predicting Species of Animal based on Lineage
Programming Assignment: Predicting Sepal Width
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1
Programming
- Predicting Sepal Width
Auto Summary
Explore advanced AI techniques with Coursera's "Neural Networks and Random Forests" course. Dive into neural networks, learn to code models, and master overfitting prevention. Transition to random forests and their unique origins. Complete projects predicting heart disease and patient similarities. Ideal for professionals in Data Science & AI. Subscriptions: Starter, Professional. Duration: 600 minutes.
Rajvir Dua
Neelesh Tiruviluamala