Overview

Explore machine learning fundamentals through a beginner-friendly online specialisation created by DeepLearning.AI and Stanford Online. Build practical skills to develop real-world AI applications using modern machine learning techniques. Learn supervised learning concepts such as linear regression, logistic regression, neural networks and decision trees. Apply unsupervised learning methods, including clustering, dimensionality reduction and recommender systems. Discover best practices used in AI and machine learning innovation, including model evaluation, tuning and data-centric performance improvement. Gain practical know-how to apply machine learning confidently to real-world challenges. Start building a strong foundation for future learning, AI project work or career development in machine learning.

Key Takeaways

At the end of the programme, you will be able to:

Build machine learning models in Python using NumPy and scikit-learn
Build and train supervised models for prediction and binary classification, including linear and logistic regression
Build and train a neural network with TensorFlow for multi-class classification
Apply machine learning development best practices so models generalise to real-world data and tasks
Build and use decision trees and tree ensemble methods, including random forests and boosted trees
Use clustering and anomaly detection techniques for unsupervised learning
Build recommender systems using collaborative filtering and content-based deep learning
Build a deep reinforcement learning model

Who Should Attend

Please refer to the job roles section.

Prerequisites

Basic coding: for loops, functions, and if/else statements.
High school-level mathematics: arithmetic and algebra.

Machine Learning Specialization

Machine Learning Specialization

Instructors

Andrew Ng

Eddy Shyu

Aarti Bagul

Geoff Ladwig