Machine Learning Specialization

Machine Learning Specialization

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 21 languages available Some content may not be translated Instructors: Andrew Ng +3 more Close Instructors Top Instructor Andrew Ng Stanford University 42 Courses

Description

Machine learning, a subset of artificial intelligence, is a rapidly growing field in the world of technology. With the ability to analyze and interpret large amounts of data, machine learning algorithms can be used to make predictions, identify patterns, and automate decision-making processes. As the demand for skilled professionals in machine learning continues to rise, many individuals are turning to specialized courses and programs to enhance their knowledge and skills in this area.

One such specialized program is the Machine Learning Specialization, which is designed to provide students with a comprehensive understanding of machine learning concepts and techniques. This program typically covers topics such as supervised and unsupervised learning, reinforcement learning, neural networks, and deep learning. Students are also introduced to various tools and programming languages commonly used in machine learning, such as Python, R, and TensorFlow.

The Machine Learning

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

Some content may not be translated

Andrew Ng
Geoff Ladwig
Aarti Bagul

Instructors: Andrew Ng

Top Instructor

378,317 already enrolled

Specialization – 3 course series

Get in-depth knowledge of a subject

4.9

(20,896 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

Add to your LinkedIn profile

,

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

About the author

Study on Scholarship Today -- Check your eligibility for up to 100% scholarship.