Stanford University
DeepLearning.AI

Advanced Learning Algorithms

This course is part of Machine Learning Specialization

Taught in English

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Andrew Ng
Aarti Bagul
Geoff Ladwig

Instructors: Andrew Ng

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242,012 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.9

(5,314 reviews)

|

98%

Beginner level

Recommended experience

34 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Assessments

14 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.9

(5,314 reviews)

|

98%

Beginner level

Recommended experience

34 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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).

What's included

17 videos1 reading4 quizzes1 programming assignment3 ungraded labs

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.

What's included

15 videos4 quizzes1 programming assignment5 ungraded labs

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.

What's included

17 videos3 quizzes1 programming assignment2 ungraded labs

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).

What's included

14 videos2 readings3 quizzes1 programming assignment2 ungraded labs

Instructors

Instructor ratings
5.0 (1,732 ratings)
Andrew Ng

Top Instructor

DeepLearning.AI
42 Courses7,275,151 learners

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DeepLearning.AI

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