Artificial Intelligence

A three-course specialization introducing modern machine learning through supervised models, neural networks, tree-based methods, unsupervised techniques, recommender systems, and reinforcement learning.

Specialization

Machine Learning Specialization

A beginner-friendly three-course sequence that combines machine-learning intuition with practical Python work using NumPy, scikit-learn, and TensorFlow.

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DeepLearning.AI · Stanford Online · Coursera · 3 Courses

View Complete Specialization on Coursera

Course Sequence

3 Courses

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Supervised Machine Learning: Regression and Classification

An introduction to supervised machine learning using Python, NumPy, and scikit-learn, focused on prediction and binary-classification models.

Regression

Linear regression, prediction, cost functions, model training, and feature behavior.

Classification

Logistic regression, binary classification, decision boundaries, and classification-oriented modeling.

Practical Model Development

Feature engineering, data preprocessing, model optimization, evaluation, and supervised-learning workflows in Python.

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Advanced Learning Algorithms

A continuation into neural networks, TensorFlow, machine-learning development practices, decision trees, random forests, and boosted trees.

Neural Networks

Building and training neural networks with TensorFlow for multiclass classification.

Tree-Based Methods

Decision trees, random forests, boosted trees, and tree-ensemble techniques.

Machine-Learning Practice

Model evaluation, optimization, responsible development, and practices that improve generalization to real-world data.

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Unsupervised Learning, Recommenders, Reinforcement Learning

A course covering clustering, anomaly detection, recommender systems, and introductory deep reinforcement learning.

Unsupervised Learning

Clustering, anomaly detection, and methods for identifying structure without labeled target values.

Recommender Systems

Collaborative filtering and content-based recommendation using deep-learning methods.

Reinforcement Learning

Core reinforcement-learning concepts and the construction of a deep reinforcement-learning model.