Stanford University
DeepLearning.AI
Machine Learning Specialization
Stanford University
DeepLearning.AI

Machine Learning Specialization

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

Taught in English

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Andrew Ng
Geoff Ladwig
Aarti Bagul

Instructors: Andrew Ng

Top Instructor

436,485 already enrolled

Specialization - 3 course series

Get in-depth knowledge of a subject

4.9

(23,354 reviews)

Beginner level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace
Earn degree credit

What you'll learn

  • Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)

  • Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods

  • Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection

  • Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

Details to know

Shareable certificate

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.9

(23,354 reviews)

Beginner level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace
Earn degree credit

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

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Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Stanford University
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Specialization - 3 course series

Supervised Machine Learning: Regression and Classification

Course 133 hours4.9 (19,985 ratings)

What you'll learn

  • Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn

  • Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Skills you'll gain

Category: Linear Regression
Category: Regularization to Avoid Overfitting
Category: Logistic Regression for Classification
Category: Gradient Descent
Category: Supervised Learning

Advanced Learning Algorithms

Course 234 hours4.9 (5,552 ratings)

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

Skills you'll gain

Category: Tensorflow
Category: Advice for Model Development
Category: Artificial Neural Network
Category: Xgboost
Category: Tree Ensembles

Unsupervised Learning, Recommenders, Reinforcement Learning

Course 327 hours4.9 (2,903 ratings)

What you'll learn

  • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection

  • Build recommender systems with a collaborative filtering approach and a content-based deep learning method

  • Build a deep reinforcement learning model

Skills you'll gain

Category: Anomaly Detection
Category: Unsupervised Learning
Category: Reinforcement Learning
Category: Collaborative Filtering
Category: Recommender Systems

Instructors

Andrew Ng

Top Instructor

Stanford University
44 Courses7,373,895 learners

Offered by

DeepLearning.AI

Get a head start on your degree

When you complete this Specialization, you can earn college credit if you are admitted and enroll in one of the following online degree programs.¹

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