BreakIntoAI with a Focus on Machine Learning. Learn the essentials of artificial intelligence and get useful machine learning abilities in this 3-course curriculum designed for beginners, taught by AI visionary Andrew Ng.
The Machine Learning Specialization is an online program developed jointly by DeepLearning.AI and Stanford Online. This program is designed for beginners and aims to provide a solid foundation in machine learning, as well as practical skills to build real-world AI applications.
The course is taught by Andrew Ng, a renowned AI expert who has made significant contributions to the field through his research at Stanford University, as well as his work at Google Brain, Baidu, and Landing.AI.
This Specialization consists of three courses and is an updated version of Andrew’s original Machine Learning course, which has received a rating of 4.9 out of 5 and has been taken by over 4.8 million learners since its launch in 2012.
The program covers a wide range of topics in modern machine learning, including supervised learning (such as linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (including clustering, dimensionality reduction, and recommender systems), as well as best practices used in Silicon Valley for AI and machine learning innovation (such as model evaluation and tuning, data-centric approaches to performance improvement, and more).
Upon completion of this Specialization, you will have a deep understanding of key concepts in machine learning and the practical skills to effectively apply them to real-world problems. Whether you are looking to enter the field of AI or build a career in machine learning, the new Machine Learning Specialization is the ideal starting point.
Project for Applied Learning
After completing this Specialization, you’ll be prepared to:
- Use the well-known machine learning libraries scikit-learn and NumPy to create machine learning models in Python.
- Create and hone supervised machine learning models, such as logistic and linear regression, for use in binary classification and prediction problems.
- Use TensorFlow to construct and train a neural network for multi-class classification.
- Use machine learning development best practices to ensure that your models generalize to real-world applications and data.
- Construct and apply tree ensemble techniques, such as boosted trees and random forests, and decision trees.
- Employ unsupervised learning strategies, such as anomaly detection and grouping, for unsupervised learning.
- Create recommender systems using content-based deep learning and collaborative filtering techniques.
- Create a profound reinforcement learning model.
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
