Online Course – Stanford University Certified Professional Internship in Machine Learning

Expertise – 3-part course series

The Machine Learning Mastery is a foundational online program created in collaboration between DeepLearning.AI and Stanford University. This beginner-friendly program will teach you the basics of machine learning and how to use these techniques to build real-world AI applications.

About the expertise

The expertise is delivered by Andrew Ng, an AI visionary who conducted important research at Stanford University and did groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the field of AI.

The 3-part course series is an updated version of Andrew’s groundbreaking Machine Learning course, which has received a 4.9 out of 5 rating and has been taken by over 4.8 million learners since its launch 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, recommendation systems)
• Some of the best practices used in Silicon Valley for innovation in artificial intelligence and machine learning (model evaluation, model tuning, and a data-driven approach to performance improvement, etc.)

By the end of the specialization, you will master the key concepts and have the practical knowledge to quickly and effectively apply machine learning to challenging real-world problems. If you are looking to enter the world of AI or build a career in machine learning, the new Machine Learning specialization is the best place to start.

Hands-on Learning Project

At the end of this specialization, you will be ready to:

• Build machine learning models in Python using popular libraries like NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network using TensorFlow to perform multi-layer classification.
• Implement best practices for machine learning development so that your models fit real-world data and tasks.
• Construct and use decision trees and tree-based methods, including random forests and boosted trees.
• Use unsupervised learning techniques: including clustering and anomaly detection.
• Build recommendation systems using a collaborative filtering approach and a content-based deep learning method.
• Build a deep self-learning model.