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Advanced Learning Algorithms
  • Level Foundation
  • المدة
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

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.

الوحدات

Neural networks intuition

    • 4 Videos
    • 1 Readings
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4 Videos

  • Welcome!
  • Neurons and the brain
  • Demand Prediction
  • Example: Recognizing Images

1 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Practice quiz: Neural networks intuition

    • 1 Assignment
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1 Assignment

  • Practice quiz: Neural networks intuition

Neural network model

    • 3 Videos
    • 1 Lab
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1 Labs

  • Neurons and Layers

3 Videos

  • Neural network layer
  • More complex neural networks
  • Inference: making predictions (forward propagation)

Practice quiz: Neural network model

    • 1 Assignment
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1 Assignment

  • Practice quiz: Neural network model

TensorFlow implementation

    • 3 Videos
    • 1 Lab
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1 Labs

  • Coffee Roasting in Tensorflow

3 Videos

  • Inference in Code
  • Data in TensorFlow
  • Building a neural network

Practice quiz: TensorFlow implementation

    • 1 Assignment
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1 Assignment

  • Practice quiz: TensorFlow implementation

Neural network implementation in Python

    • 2 Videos
    • 1 Lab
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1 Labs

  • CoffeeRoastingNumPy

2 Videos

  • Forward prop in a single layer
  • General implementation of forward propagation

Practice quiz: Neural network implementation in Python

    • 1 Assignment
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1 Assignment

  • Practice quiz: Neural network implementation in Python

Speculations on artificial general intelligence (AGI)

    • 1 Videos
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1 Videos

  • Is there a path to AGI?

Vectorization (optional)

    • 4 Videos
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4 Videos

  • How neural networks are implemented efficiently
  • Matrix multiplication
  • Matrix multiplication rules
  • Matrix multiplication code

Practice Lab: Neural networks

    • 1 Programming
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1 Programming

  • Practice Lab: Neural Networks for Binary Classification

Neural Network Training

    • 2 Videos
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2 Videos

  • TensorFlow implementation
  • Training Details

Practice quiz: Neural Network Training

    • 1 Assignment
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1 Assignment

  • Practice quiz: Neural Network Training

Activation Functions

    • 3 Videos
    • 1 Lab
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1 Labs

  • ReLU activation

3 Videos

  • Alternatives to the sigmoid activation
  • Choosing activation functions
  • Why do we need activation functions?

Practice quiz: Activation Functions

    • 1 Assignment
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1 Assignment

  • Practice quiz: Activation Functions

Multiclass Classification

    • 5 Videos
    • 2 Lab
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2 Labs

  • Softmax
  • Multiclass

5 Videos

  • Multiclass
  • Softmax
  • Neural Network with Softmax output
  • Improved implementation of softmax
  • Classification with multiple outputs (Optional)

Practice quiz: Multiclass Classification

    • 1 Assignment
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1 Assignment

  • Practice quiz: Multiclass Classification

Additional Neural Network Concepts

    • 2 Videos
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2 Videos

  • Advanced Optimization
  • Additional Layer Types

Practice quiz: Additional Neural Network Concepts

    • 1 Assignment
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1 Assignment

  • Practice quiz: Additional Neural Network Concepts

Back Propagation (Optional)

    • 3 Videos
    • 2 Lab
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2 Labs

  • Optional Lab: Derivatives
  • Optional Lab: Back propagation

3 Videos

  • What is a derivative? (Optional)
  • Computation graph (Optional)
  • Larger neural network example (Optional)

Practice Lab: Neural network training

    • 1 Programming
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1 Programming

  • Practice Lab: Neural Networks for Multiclass classification

Advice for applying machine learning

    • 3 Videos
    • 1 Lab
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1 Labs

  • Optional Lab: Model Evaluation and Selection

3 Videos

  • Deciding what to try next
  • Evaluating a model
  • Model selection and training/cross validation/test sets

Practice quiz: Advice for applying machine learning

    • 1 Assignment
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1 Assignment

  • Practice quiz: Advice for applying machine learning

Bias and variance

    • 6 Videos
    • 1 Lab
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1 Labs

  • Optional Lab: Diagnosing Bias and Variance

6 Videos

  • Diagnosing bias and variance
  • Regularization and bias/variance
  • Establishing a baseline level of performance
  • Learning curves
  • Deciding what to try next revisited
  • Bias/variance and neural networks

Practice quiz: Bias and variance

    • 1 Assignment
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1 Assignment

  • Practice quiz: Bias and variance

Machine learning development process

    • 6 Videos
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6 Videos

  • Iterative loop of ML development
  • Error analysis
  • Adding data
  • Transfer learning: using data from a different task
  • Full cycle of a machine learning project
  • Fairness, bias, and ethics

Practice quiz: Machine learning development process

    • 1 Assignment
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1 Assignment

  • Practice quiz: Machine learning development process

Skewed datasets (optional)

    • 2 Videos
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2 Videos

  • Error metrics for skewed datasets
  • Trading off precision and recall

Practice Lab: Advice for applying machine learning

    • 1 Programming
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1 Programming

  • Practice Lab: Advice for Applying Machine Learning

Decision trees

    • 2 Videos
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2 Videos

  • Decision tree model
  • Learning Process

Practice quiz: Decision trees

    • 1 Assignment
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1 Assignment

  • Practice quiz: Decision trees

Decision tree learning

    • 6 Videos
    • 1 Lab
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1 Labs

  • Optional Lab: Decision Trees

6 Videos

  • Measuring purity
  • Choosing a split: Information Gain
  • Putting it together
  • Using one-hot encoding of categorical features
  • Continuous valued features
  • Regression Trees (optional)

Practice quiz: Decision tree learning

    • 1 Assignment
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1 Assignment

  • Practice quiz: Decision tree learning

Tree ensembles

    • 5 Videos
    • 1 Lab
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1 Labs

  • Optional Lab: Tree Ensembles

5 Videos

  • Using multiple decision trees
  • Sampling with replacement
  • Random forest algorithm
  • XGBoost
  • When to use decision trees

Practice quiz: Tree ensembles

    • 1 Assignment
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1 Assignment

  • Practice quiz: Tree ensembles

End of Access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Practice Lab: Decision Trees

    • 1 Programming
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1 Programming

  • Practice Lab: Decision Trees

Conversations with Andrew (Optional)

    • 1 Videos
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1 Videos

  • Andrew Ng and Chris Manning on Natural Language Processing

Acknowledgments

    • 1 Readings
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1 Readings

  • Acknowledgements

Auto Summary

Advance your personal development with "Advanced Learning Algorithms," a course by AI expert Andrew Ng. Focused on machine learning, this course covers neural networks, decision trees, and more using TensorFlow. This foundational course, part of a 3-course Machine Learning Specialization by DeepLearning.AI and Stanford Online, is perfect for beginners aiming to master AI applications. Available on Coursera with subscription options, it offers hands-on training to build a career in AI.

Andrew Ng
Instructors

Andrew Ng

Aarti Bagul
Instructors

Aarti Bagul

Geoff Ladwig
Instructors

Geoff Ladwig