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

Andrew Ng
Geoff Ladwig
Aarti Bagul

Instructors: Andrew Ng

Top Instructor

518,258 already enrolled

Get in-depth knowledge of a subject
4.9

(27,953 reviews)

Beginner level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.9

(27,953 reviews)

Beginner level

Recommended experience

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

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

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Taught in English

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Specialization - 3 course series

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

Advanced Learning Algorithms

Course 234 hours

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

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

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Instructors

Andrew Ng

Top Instructor

Stanford University
45 Courses7,787,148 learners

Offered by

DeepLearning.AI

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Frequently asked questions

¹ Median salary and job opening data are sourced from United States Lightcast™ Job Postings Report. Data for job roles relevant to featured programs (3/1/2023 - 3/1/2024)