Machine Learning Specialization

Learning Outcomes

By the end of this specialization, learners will be able to:

• Build and train supervised machine learning models for prediction and classification (linear and logistic regression).

• Develop neural networks using TensorFlow for multi-class classification.

• Apply best practices for model evaluation, tuning, and generalization.

• Implement decision trees, random forests, and boosted trees.

• Use clustering and anomaly detection for unsupervised learning tasks.

• Create recommender systems using collaborative filtering and deep learning.

• Build and train deep reinforcement learning models.

Skills You’ll Gain

• Machine Learning

• Feature Engineering

• Deep Learning

• Predictive Modeling

• Decision Trees and Random Forests

• Unsupervised and Reinforcement Learning

• Python Programming

Tools Covered

• NumPy

• Scikit-learn

• TensorFlow

• Jupyter Notebook

• Python

Courses in the Specialization

Career Relevance

Prepares learners for roles such as Machine Learning Engineer or AI Specialist.
Offers a comprehensive foundation in modern AI applications.
Provides a career certificate from Stanford University and DeepLearning.AI.