Now the launch of a New, Machine Learning Specialization by Andrew NG!

#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, CEO and founder of Landing AI, co-founder of Coursera, and adjunct professor at Stanford University, is launching a new Machine Learning Specialization. This updated and expanded course, a collaboration between DeepLearning.AI and Stanford University, builds on Andrew’s original course, which was one of Coursera’s most popular, attracting nearly 5 million learners worldwide.

In this new specialization, learners will:

• Develop machine learning models using NumPy and scikit-learn, and create and train supervised models for tasks such as prediction and binary classification using techniques like linear and logistic regression.
• Build and train neural networks with TensorFlow to handle multi-class classification, and develop decision trees and tree ensemble methods.
• Apply best practices for machine learning development and utilize unsupervised learning techniques, including clustering and anomaly detection.
• Construct recommender systems using both collaborative filtering and content-based deep learning approaches, and design a deep reinforcement learning model.

This specialization encapsulates the significant advancements made in machine learning over the past decade since the original course was launched. It includes:

• Updated graded assignments and lectures that teach Python instead of Octave/Matlab.
• Three comprehensive courses that provide a broad introduction to machine learning, supervised learning, and unsupervised learning.
• Numerous additional code notebooks and interactive graphs to enhance learners’ understanding of key concepts.

What You’ll Learn:

• How to develop machine learning models using NumPy and scikit-learn, and create and train supervised models for tasks like prediction and binary classification (linear and logistic regression).
• How to build and train neural networks with TensorFlow for multi-class classification, and how to develop decision trees and tree ensemble methods.
• How to apply best practices for machine learning development and utilize unsupervised learning techniques for tasks such as clustering and anomaly detection.
• How to build recommender systems using collaborative filtering and content-based deep learning methods, as well as how to design and implement a deep reinforcement learning model.